CTP differences
Chloe Yap
29 December 2022
To run: Beforehand:
module load pandocIn R:
setwd("~/shared-gandalm/brain_CTP/Scripts/integration/ctp_differences")
rmarkdown::render("ctp_differences.Rmd", "html_document")1 Set-up
1.1 Directories and libraries
library(tidyverse)
library(rmarkdown) # You need this library to run this template.
library(epuRate) # Install with remotes::install_github("holtzy/epuRate", force=TRUE) - use this instead of devtools::install_github()
library(reshape2)
library(compositions)
library(wesanderson)
library(rms)
library(lmtest)
#library(ashr)
#library(mashr)
library(broom)study <- "Jaffe2018"
data_dir <- paste("~/shared-gandalm/brain_CTP/Data/methylation/", study, "/analysis", sep = "")
filen <- "Jaffe2018_age0"
scz_dir <- paste(data_dir, "/", filen, "_ctp_pheno.txt", sep = "")
scz_adjoligo_dir <- paste(data_dir, "/", "hseq_adjoligo_ctp_pheno.raw.txt", sep = "")
study <- "ASD_methylation_brain"
data_dir <- paste("~/shared-gandalm/brain_CTP/Data/methylation/", study, "/analysis", sep = "")
filen <- "KCL_R01MH094714_ASD_Illumina450K_PFC"
asd_dir <- paste(data_dir, "/", filen, "_ctp_pheno.txt", sep = "")
asd_adjoligo_dir <- paste(data_dir, "/", "hseq_adjoligo_ctp_pheno.raw.txt", sep = "")
study <- "ROSMAP"
data_dir <- paste("~/shared-gandalm/brain_CTP/Data/methylation/", study, "/analysis", sep = "")
#filen <- "ROSMAP_ComBatplate_neg0"
filen <- "ROSMAP"
azd_dir <- paste(data_dir, "/", filen, "_ctp_pheno.txt", sep = "")
azd_adjoligo_dir <- paste(data_dir, "/", "hseq_adjoligo_ctp_pheno.raw.txt", sep = "")
out_dir <- "~/shared-gandalm/brain_CTP/Data/integration/ctp_differences"1.2 Read-in data
1.2.1 All CTPs
# All CTPs
scz <- read.delim(scz_dir, header = T, as.is = T)
asd <- read.delim(asd_dir, header = T, as.is = T)
azd <- read.delim(azd_dir, header = T, as.is = T)
# Adjust for oligodendrocytes
scz_adjoligo <- read.delim(scz_adjoligo_dir, header = T, as.is = T)
asd_adjoligo <- read.delim(asd_adjoligo_dir, header = T, as.is = T)
azd_adjoligo <- read.delim(azd_adjoligo_dir, header = T, as.is = T)1.2.1.1 ASD study: subselect for better group comparison
Subselect for better group comparison
- based on the stats shown, try removing the youngest 12 participants (all ASD) + removing the oldest 6 participants (all CTL), for the purposes of within-study comparisons
- justification:
- for LIBD, only took participants older than 18 (since differences in CTPs may be expected in this age group)
- there are more ASD than CTL participants, so this should help balance the design a bit better
foo <- summary(asd %>% filter(RegionID == "BA9") %>% pull(age))
asd %>% filter(RegionID == "BA9") %>% group_by(group) %>% dplyr::summarise(Q1 = length(which(age >= foo[1] & age <= foo[2])), Q2 = length(which(age > foo[2] & age <= foo[3])), Q3 = length(which(age > foo[3] & age <= foo[5])), Q4 = length(which(age > foo[5] & age <= foo[6])))## # A tibble: 2 × 5
## group Q1 Q2 Q3 Q4
## <chr> <int> <int> <int> <int>
## 1 ASD 16 13 10 4
## 2 CTL 3 7 8 15asd %>% arrange(age) %>% dplyr::select(c("IID", "age", "group")) %>% head(20)## IID age group
## 1 AN03345_BA9 2 ASD
## 2 UMB5308_BA9 4 ASD
## 3 AN08873_BA9 5 ASD
## 4 AN13872_BA9 5 ASD
## 5 UMB4849_BA9 7 ASD
## 6 AN19511_BA9 8 ASD
## 7 AN16641_BA9 9 ASD
## 8 AN14762_BA9 9 ASD
## 9 AN06365_BA9 10 ASD
## 10 AN09402_BA9 11 ASD
## 11 AN16115_BA9 11 ASD
## 12 AN04682_BA9 15 ASD
## 13 UMB5242_BA9 15 CTL
## 14 AN02987_BA9 15 ASD
## 15 UMB5302_BA9 16 ASD
## 16 AN07444_BA9 17 CTL
## 17 AN01570_BA9 18 ASD
## 18 AN03935_BA9 19 ASD
## 19 AN03217_BA9 19 CTL
## 20 UMB4590_BA9 20 CTLasd %>% arrange(desc(age)) %>% dplyr::select(c("IID", "age", "group")) %>% head(20)## IID age group
## 1 NP115_09_BA9 71 CTL
## 2 NP053_06_BA9 71 CTL
## 3 NP168_08_BA9 71 CTL
## 4 x1148_98_BA9 70 CTL
## 5 x1340_94_BA9 69 CTL
## 6 NP071_10_BA9 68 CTL
## 7 UMB5303_BA9 67 ASD
## 8 x1085_93_BA9 67 CTL
## 9 NP122_11_BA9 65 CTL
## 10 x1127_93_BA9 64 CTL
## 11 NP92_11_BA9 63 CTL
## 12 NP94_11_BA9 62 CTL
## 13 AN10723_BA9 60 CTL
## 14 AN09714_BA9 60 ASD
## 15 x1054_97_BA9 59 CTL
## 16 AN11864_BA9 57 CTL
## 17 NP81_11_BA9 56 CTL
## 18 AN01093_BA9 56 ASD
## 19 AN17515_BA9 54 ASD
## 20 UMB1578_BA9 53 CTL# Choose to remove the youngest 12 people (all in ASD group)
asd_exclude <- rbind(asd %>% arrange(age) %>% head(12) %>% dplyr::select("IID"),
asd %>% arrange(desc(age)) %>% head(6) %>% dplyr::select("IID"))
write.table(asd_exclude[,c(1,1)], "~/shared-gandalm/brain_CTP/Data/integration/ctp_differences/asd_exclude_studybalance.id", col.names = F, row.names = F, sep = "\t", quote = F)1.2.1.2 SCZ study: subselect for better group comparison
Subselect for better group comparison
- take people aged 18+
foo <- summary(scz %>% pull(age))
scz %>% group_by(group) %>% dplyr::summarise(Q1 = length(which(age >= foo[1] & age <= foo[2])), Q2 = length(which(age > foo[2] & age <= foo[3])), Q3 = length(which(age > foo[3] & age <= foo[5])), Q4 = length(which(age > foo[5] & age <= foo[6])))## # A tibble: 2 × 5
## group Q1 Q2 Q3 Q4
## <chr> <int> <int> <int> <int>
## 1 Control 107 68 60 54
## 2 SCZ 12 51 58 65scz %>% arrange(age) %>% dplyr::select(c("IID", "age", "group")) %>% filter(age <= 18)## IID age group
## 1 Sample66_Control 0.002739 Control
## 2 Sample67_Control 0.005479 Control
## 3 Sample68_Control 0.068493 Control
## 4 Sample69_Control 0.095890 Control
## 5 Sample70_Control 0.109589 Control
## 6 Sample72_Control 0.202739 Control
## 7 Sample73_Control 0.216438 Control
## 8 Sample75_Control 0.246575 Control
## 9 Sample76_Control 0.249315 Control
## 10 Sample77_Control 0.290410 Control
## 11 Sample79_Control 0.304109 Control
## 12 Sample81_Control 0.326027 Control
## 13 Sample85_Control 0.326027 Control
## 14 Sample88_Control 0.353424 Control
## 15 Sample89_Control 0.364383 Control
## 16 Sample92_Control 0.391780 Control
## 17 Sample94_Control 0.402739 Control
## 18 Sample96_Control 0.408219 Control
## 19 Sample97_Control 0.482191 Control
## 20 Sample98_Control 0.515068 Control
## 21 Sample102_Control 1.339726 Control
## 22 Sample103_Control 1.624657 Control
## 23 Sample104_Control 2.024657 Control
## 24 Sample106_Control 2.490410 Control
## 25 Sample107_Control 2.715068 Control
## 26 Sample109_Control 3.046575 Control
## 27 Sample110_Control 3.980821 Control
## 28 Sample112_Control 4.139726 Control
## 29 Sample114_Control 4.208219 Control
## 30 Sample115_Control 4.652054 Control
## 31 Sample116_Control 4.709589 Control
## 32 Sample117_Control 5.345205 Control
## 33 Sample119_Control 6.339726 Control
## 34 Sample121_Control 8.254794 Control
## 35 Sample122_Control 9.156164 Control
## 36 Sample124_Control 10.726027 Control
## 37 Sample125_Control 12.282191 Control
## 38 Sample127_Control 12.879452 Control
## 39 Sample128_Control 13.032876 Control
## 40 Sample129_Control 13.106849 Control
## 41 Sample130_Control 13.169863 Control
## 42 Sample131_Control 13.336986 Control
## 43 Sample132_Control 13.421917 Control
## 44 Sample133_Control 13.487671 Control
## 45 Sample134_Control 13.698630 Control
## 46 Sample135_Control 13.997260 Control
## 47 Sample136_Control 14.101369 Control
## 48 Sample137_Control 14.553424 Control
## 49 Sample138_Control 14.632876 Control
## 50 Sample139_Control 14.873972 Control
## 51 Sample140_Control 15.128767 Control
## 52 Sample141_Control 15.158904 Control
## 53 Sample142_Control 15.172602 Control
## 54 Sample143_Control 15.232876 Control
## 55 Sample144_Control 15.331506 Control
## 56 Sample145_Control 15.460273 Control
## 57 Sample146_Control 15.493150 Control
## 58 Sample148_Control 15.800000 Control
## 59 Sample151_Control 16.183561 Control
## 60 Sample153_Control 16.353424 Control
## 61 Sample154_Control 16.583561 Control
## 62 Sample156_Control 16.649315 Control
## 63 Sample157_Control 16.695890 Control
## 64 Sample158_Control 16.726027 Control
## 65 Sample159_Control 16.753424 Control
## 66 Sample162_Control 16.800000 Control
## 67 Sample163_Control 16.934246 Control
## 68 Sample165_Control 17.090410 Control
## 69 Sample167_Control 17.219178 Control
## 70 Sample168_Control 17.235616 Control
## 71 Sample169_Schizo 17.238356 SCZ
## 72 Sample171_Control 17.279452 Control
## 73 Sample172_Control 17.764383 Control# Choose to remove the youngest 12 people (all in ASD group)
scz_exclude <- scz %>% filter(age <= 18) %>% dplyr::select(c("IID"))
scz_exclude$IID <- unlist(lapply(strsplit(scz_exclude$IID, "_"), function(x) x[1]))
write.table(scz_exclude[,c(1,1)], "~/shared-gandalm/brain_CTP/Data/integration/ctp_differences/scz_exclude_studybalance.id", col.names = F, row.names = F, sep = "\t", quote = F)1.3 Prep to merge
1.3.1 Same names for columns
# Diagnosis variables
scz$dx <- ifelse(scz$group == "SCZ", "SCZ", "CTL")
asd$dx <- ifelse(asd$Diagnosis == "CTL", "CTL", "ASD")
azd$dx <- ifelse(azd$group == "CTL", "CTL", "AZD")
scz_adjoligo$dx <- ifelse(scz_adjoligo$group == "SCZ", "SCZ", "CTL")
asd_adjoligo$dx <- ifelse(asd_adjoligo$group == "CTL", "CTL", "ASD")
azd_adjoligo$dx <- ifelse(azd_adjoligo$group == "CTL", "CTL", "AZD")
# Batch variable (take the batch variable with biggest effect)
scz$batch <- scz$plate
asd$batch <- paste("ASDbrain", asd$Batch, sep = "")
#azd$batch <- azd$Sample_Plate
azd$batch <- paste("ROSMAP", azd$batch, sep = "")
scz_adjoligo$batch <- scz_adjoligo$plate
asd_adjoligo$batch <- asd_adjoligo$Batch
azd_adjoligo$batch <- azd_adjoligo$Sample_Plate
# Study variable
scz$study <- "LIBD"
asd$study <- "ASDbrain"
azd$study <- "ROSMAP"
scz_adjoligo$study <- "LIBD"
asd_adjoligo$study <- "ASDbrain"
azd_adjoligo$study <- "ROSMAP"
# Amend IID for SCZ
scz$IID <- unlist(lapply(strsplit(scz$IID, "_"), function(x) x[1]))
scz_adjoligo$IID <- unlist(lapply(strsplit(scz_adjoligo$IID, "_"), function(x) x[1]))
# Rename cell-types from the deconvolution of interest
colnames(scz) <- gsub("hseq_", "", colnames(scz))
colnames(asd) <- gsub("hseq_", "", colnames(asd))
colnames(azd) <- gsub("hseq_", "", colnames(azd))
colnames(scz_adjoligo) <- gsub("hseq_", "", colnames(scz_adjoligo))
colnames(asd_adjoligo) <- gsub("hseq_", "", colnames(asd_adjoligo))
colnames(azd_adjoligo) <- gsub("hseq_", "", colnames(azd_adjoligo))
# Add age2
asd$age2 <- asd$age^2
scz$age2 <- scz$age^2
azd$age2 <- azd$age^2
asd_adjoligo$age2 <- asd_adjoligo$age^2
scz_adjoligo$age2 <- scz_adjoligo$age^2
azd_adjoligo$age2 <- azd_adjoligo$age^21.3.2 Take hseq data
ctp_cols <- c("Exc", "Inh", "Astro", "Endo", "Micro", "Oligo", "OPC", "Neuron", "Glial")
level2_cols <- ctp_cols[-which(ctp_cols %in% c("Neuron", "Glial"))]
glial_rmoligo_n <- level2_cols[-which(level2_cols %in% c("Exc", "Inh", "Oligo"))]
neuron_n <- c("Exc", "Inh")
level2_rmoligo_cols <- c(neuron_n, glial_rmoligo_n)
ctp_rmoligo_cols <- c(level2_rmoligo_cols, "Neuron", "Glial")
keepcol_scz <- c("IID", ctp_cols, "dx", "age", "age2", "sex", "batch", "study")
keepcol_scz_adjoligo <- c("IID", ctp_rmoligo_cols, "dx", "age", "age2", "sex", "batch", "study")
#keepcol_scz <- c("IID", "Exc", "Inh", "NonN_Astro_FGF3R", "NonN_Micro.Endo_TYROBP", "NonN_Oligo_MBP", "group", "age", "sex", "race", "plate")
keepcol_asd <- c("IID", ctp_cols, "dx", "age", "age2", "sex", "batch", "study")
keepcol_asd_adjoligo <- c("IID", ctp_rmoligo_cols, "dx", "age", "age2", "sex", "batch", "study")
#keepcol_asd <- c("IID", "Exc", "Inh", "NonN_Astro_FGF3R", "NonN_Micro.Endo_TYROBP", "NonN_Oligo_MBP", "Diagnosis", "age", "sex", "Batch", "PMI", "BrainBank")
keepcol_azd <- c("IID", ctp_cols, "dx", "age", "age2", "sex", "batch", "study")
keepcol_azd_adjoligo <- c("IID", ctp_rmoligo_cols, "dx", "age", "age2", "sex", "batch", "study")
#keepcol_azd <- c("IID", "Exc", "Inh", "NonN_Astro_FGF3R", "NonN_Micro.Endo_TYROBP", "NonN_Oligo_MBP", "group", "braaksc", "ceradsc", "cogdx", "dcfdx_lv", "age", "sex", "batch", "Sample_Plate", "study")1.3.3 Join into 1 dataframe
ctp <- rbind(scz[,keepcol_scz], asd[,keepcol_asd])
ctp <- rbind(ctp, azd[,keepcol_azd])
# Adjust for oligodendrocytes
ctp_adjoligo <- rbind(scz_adjoligo[,keepcol_scz_adjoligo], asd_adjoligo[,keepcol_asd_adjoligo])
ctp_adjoligo <- rbind(ctp_adjoligo, azd_adjoligo[,keepcol_azd_adjoligo])1.3.4 Stacked bar charts
ctp.tmp <- ctp %>% group_by(dx) %>% dplyr::arrange(Glial, .by_group = TRUE) %>% mutate(plot_id = paste(dx, Glial, sep = ""))
ctp.tmp$plot_id <- paste(ctp.tmp$plot_id, 1:nrow(ctp.tmp), sep = "")
ctp.tmp$plot_id <- ifelse(ctp.tmp$dx != "CTL", paste("a", ctp.tmp$plot_id, sep = ""), ctp.tmp$plot_id)
ctp.long <- melt(ctp.tmp, id.vars = c("IID", "plot_id", "dx", "age", "age2", "sex", "batch", "study", "Neuron", "Glial"), variable.name = "celltype", value.name = "CTP")
ctp.long$celltype <- as.character(ctp.long$celltype)
ctp.long$celltype[grep("NonN", ctp.long$celltype)] <- unlist(lapply(strsplit(ctp.long$celltype[grep("NonN", ctp.long$celltype)], "_"), function(x) x[2]))
# - order
ctp.long$MatchDx <- match(ctp.long$dx, c("CTL", "ASD", "SCZ", "AZD"))
ctp.long$MatchStudy <- match(ctp.long$study, c("ASDbrain", "LIBD", "ROSMAP"))
ctp.long$MatchCell <- match(ctp.long$celltype, unique(ctp.long$celltype))
ctp.long$MatchID <- match(ctp.long$plot_id, ctp.long$plot_id)
ctp.long$group <- ifelse(ctp.long$dx == "CTL", "CTL", "Dx")
# Plot
stackedbar <- ctp.long %>% mutate(dx = fct_reorder(dx, MatchDx)) %>% mutate(study = fct_reorder(study, MatchStudy)) %>% mutate(celltype = fct_reorder(celltype, MatchCell)) %>%
ggplot(aes(x = plot_id, y = CTP, fill = celltype)) +
geom_bar(position="stack", stat="identity") +
scale_fill_manual(values = wes_palette(name = "Zissou1", 7, type = "continuous"), name = "") +
theme(axis.title.x = element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
facet_grid(~ study + dx, scales = "free_x", space = 'free_x')
stackedbar
ggsave(paste(out_dir, "/ctp_raw_stackedbar_ROSMAP_ASDbrain_LIBD.png", sep = ""), stackedbar, height = 8, width = 12)
ggsave(paste(out_dir, "/ctp_raw_stackedbar_ROSMAP_ASDbrain_LIBD.svg", sep = ""), stackedbar, height = 8, width = 12)1.4 clr transformation
# Keep oligodendrocytes
keep_cols <- c("IID", "dx", "age", "age2", "sex", "batch", "study")
ctp.tmp.clr <- as.matrix(ctp %>% dplyr::select(c(all_of(level2_cols))))
# - Make minimum CTP = 1e-3
length(which(ctp.tmp.clr <= 1e-3)) # check there are no 0s## [1] 4ctp.tmp.clr0 <- ctp.tmp.clr
ctp.tmp.clr0[which(ctp.tmp.clr0 <= 1e-3)] <- 1e-3
# - Perform clr-transform
ctp.tmp.clr0.tr <- clr(ctp.tmp.clr0)
ctp.clr.1e3 <- data.frame(ctp[,keep_cols], ctp.tmp.clr0.tr)
# Adjust for oligodendrocytes
keep_cols <- c("IID", "dx", "age", "age2", "sex", "batch", "study")
ctp_adjoligo.tmp.clr <- as.matrix(ctp_adjoligo %>% dplyr::select(c(all_of(neuron_n), all_of(glial_rmoligo_n))))
# Make minimum CTP = 1e-3
length(which(ctp_adjoligo.tmp.clr <= 1e-3)) # check there are no 0s## [1] 4ctp_adjoligo.tmp.clr0 <- ctp_adjoligo.tmp.clr
ctp_adjoligo.tmp.clr0[which(ctp_adjoligo.tmp.clr0 <= 1e-3)] <- 1e-3
# Perform clr-transform
ctp_adjoligo.tmp.clr0.tr <- clr(ctp_adjoligo.tmp.clr0)
ctp_adjoligo.clr.1e3 <- data.frame(ctp[,keep_cols], ctp_adjoligo.tmp.clr0.tr)1.5 Inverse Normal Transform (INT)
- N.B. doesn’t matter if you do it on the RAW or CLR-transformed CTPs!
int <- function(x) {
qnorm((rank(x,na.last="keep")-0.5)/sum(!is.na(x)))
}
# Keep oligodendrocytes
keep_cols <- c("IID", "dx", "age", "age2", "sex", "batch", "study")
# - Perform INT-transform
ctp.int.tmp <- apply(as.matrix(ctp.clr.1e3 %>% dplyr::select(c(all_of(level2_cols)))), 2, int)
ctp.int <- data.frame(ctp[,keep_cols], ctp.int.tmp)
# Adjust for oligodendrocytes
keep_cols <- c("IID", "dx", "age", "age2", "sex", "batch", "study")
# Perform INT-transform
ctp_adjoligo.int.tmp <- apply(as.matrix(ctp_adjoligo.clr.1e3 %>% dplyr::select(c(all_of(neuron_n), all_of(glial_rmoligo_n)))), 2, int)
ctp_adjoligo.int <- data.frame(ctp[,keep_cols], ctp_adjoligo.int.tmp)1.5.1 Add MatchOrder column
ctp$MatchOrder <- match(ctp$dx, c("CTL", "ASD", "SCZ", "AZD"))
ctp_adjoligo$MatchOrder <- match(ctp_adjoligo$dx, c("CTL", "ASD", "SCZ", "AZD"))
ctp.clr.1e3$MatchOrder <- match(ctp.clr.1e3$dx, c("CTL", "ASD", "SCZ", "AZD"))
ctp_adjoligo.clr.1e3$MatchOrder <- match(ctp_adjoligo.clr.1e3$dx, c("CTL", "ASD", "SCZ", "AZD"))1.6 Write output
write.table(ctp, paste(out_dir, "/ctp_noclr_aggregated_scz_asd_azd.txt", sep = ""), col.names = T, row.names = F, quote = F, sep = "\t")
write.table(ctp_adjoligo, paste(out_dir, "/ctp_adjoligo_noclr_aggregated_scz_asd_azd.txt", sep = ""), col.names = T, row.names = F, quote = F, sep = "\t")
write.table(ctp.clr.1e3, paste(out_dir, "/ctp_clr1e-3_aggregated_scz_asd_azd.txt", sep = ""), col.names = T, row.names = F, quote = F, sep = "\t")
write.table(ctp_adjoligo.clr.1e3, paste(out_dir, "/ctp_adjoligo_clr1e-3_aggregated_scz_asd_azd.txt", sep = ""), col.names = T, row.names = F, quote = F, sep = "\t")
write.table(ctp.int, paste(out_dir, "/ctp_int_aggregated_scz_asd_azd.txt", sep = ""), col.names = T, row.names = F, quote = F, sep = "\t")
write.table(ctp_adjoligo.int, paste(out_dir, "/ctp_adjoligo_int_aggregated_scz_asd_azd.txt", sep = ""), col.names = T, row.names = F, quote = F, sep = "\t")1.7 Long format
ctp.long <- melt(ctp, id.vars = c("IID", "dx", "age", "age2", "sex", "batch", "study", "MatchOrder"), variable.name = "celltype", value.name = "CTP")
ctp.long$Level <- ifelse(ctp.long$celltype %in% c("Neuron", "Glial"), "Level1", "Level2")
ctp.long$MatchOrder <- match(ctp.long$dx, c("CTL", "ASD", "SCZ", "AZD"))
ctp.long$MatchCTP <- match(ctp.long$celltype, unique(c(ctp.long %>% filter(Level == "Level1") %>% pull(celltype) %>% as.character(), ctp.long %>% filter(Level == "Level2") %>% pull(celltype) %>% as.character())))
ctp.long$celltype <- as.character(ctp.long$celltype)
ctp.long$celltype[grep("NonN", ctp.long$celltype)] <- unlist(lapply(strsplit(ctp.long$celltype[grep("NonN", ctp.long$celltype)], "_"), function(x) x[2]))
ctp_adjoligo.long <- melt(ctp_adjoligo, id.vars = c("IID", "dx", "age", "age2", "sex", "batch", "study", "MatchOrder"), variable.name = "celltype", value.name = "CTP")
ctp_adjoligo.long$Level <- ifelse(ctp_adjoligo.long$celltype %in% c("Neuron", "Glial"), "Level1", "Level2")
ctp_adjoligo.long$MatchOrder <- match(ctp_adjoligo.long$dx, c("CTL", "ASD", "SCZ", "AZD"))
ctp_adjoligo.long$MatchCTP <- match(ctp_adjoligo.long$celltype, unique(c(ctp_adjoligo.long %>% filter(Level == "Level1") %>% pull(celltype) %>% as.character(), ctp_adjoligo.long %>% filter(Level == "Level2") %>% pull(celltype) %>% as.character())))
ctp_adjoligo.long$celltype <- as.character(ctp_adjoligo.long$celltype)
ctp_adjoligo.long$celltype[grep("NonN", ctp_adjoligo.long$celltype)] <- unlist(lapply(strsplit(ctp_adjoligo.long$celltype[grep("NonN", ctp_adjoligo.long$celltype)], "_"), function(x) x[2]))
ctp.clr.1e3.long <- melt(ctp.clr.1e3, id.vars = c("IID", "dx", "age", "age2", "sex", "batch", "study", "MatchOrder"), variable.name = "celltype", value.name = "CTP")
ctp.clr.1e3.long$Level <- ifelse(ctp.clr.1e3.long$celltype %in% c("Neuron", "Glial"), "Level1", "Level2")
ctp.clr.1e3.long$MatchOrder <- match(ctp.clr.1e3.long$dx, c("CTL", "ASD", "SCZ", "AZD"))
ctp.clr.1e3.long$MatchCTP <- match(ctp.clr.1e3.long$celltype, unique(c(ctp.clr.1e3.long %>% filter(Level == "Level1") %>% pull(celltype) %>% as.character(), ctp.clr.1e3.long %>% filter(Level == "Level2") %>% pull(celltype) %>% as.character())))
ctp.clr.1e3.long$celltype <- as.character(ctp.clr.1e3.long$celltype)
ctp.clr.1e3.long$celltype[grep("NonN", ctp.clr.1e3.long$celltype)] <- unlist(lapply(strsplit(ctp.clr.1e3.long$celltype[grep("NonN", ctp.clr.1e3.long$celltype)], "_"), function(x) x[2]))
ctp_adjoligo.clr.1e3.long <- melt(ctp_adjoligo.clr.1e3, id.vars = c("IID", "dx", "age", "age2", "sex", "batch", "study", "MatchOrder"), variable.name = "celltype", value.name = "CTP")
ctp_adjoligo.clr.1e3.long$Level <- ifelse(ctp_adjoligo.clr.1e3.long$celltype %in% c("Neuron", "Glial"), "Level1", "Level2")
ctp_adjoligo.clr.1e3.long$MatchOrder <- match(ctp_adjoligo.clr.1e3.long$dx, c("CTL", "ASD", "SCZ", "AZD"))
ctp_adjoligo.clr.1e3.long$MatchCTP <- match(ctp_adjoligo.clr.1e3.long$celltype, unique(c(ctp_adjoligo.clr.1e3.long %>% filter(Level == "Level1") %>% pull(celltype) %>% as.character(), ctp_adjoligo.clr.1e3.long %>% filter(Level == "Level2") %>% pull(celltype) %>% as.character())))
ctp_adjoligo.clr.1e3.long$celltype <- as.character(ctp_adjoligo.clr.1e3.long$celltype)
ctp_adjoligo.clr.1e3.long$celltype[grep("NonN", ctp_adjoligo.clr.1e3.long$celltype)] <- unlist(lapply(strsplit(ctp_adjoligo.clr.1e3.long$celltype[grep("NonN", ctp_adjoligo.clr.1e3.long$celltype)], "_"), function(x) x[2]))2 Plots of differences
2.1 No clr, no adjustment for oligodendrocytes
ctp.long %>% filter(!IID %in% asd_exclude$IID) %>% filter(!IID %in% scz_exclude$IID) %>%
mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = fct_rev(celltype), y = CTP, fill = dx)) +
geom_boxplot(position = position_dodge(.8),alpha=.8,outlier.size = 0)+
geom_point(aes(colour = dx), position = position_jitterdodge(.1),alpha=.3) +
coord_flip() + theme_bw() + xlab("") +
scale_fill_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
facet_grid(Level ~ study, scales = "free_y", space = 'free_y')
ggsave(paste(out_dir, "/ctp_differences_bygroup_raw.png", sep = ""))## Saving 7 x 5 in imageggsave(paste(out_dir, "/ctp_differences_bygroup_raw.svg", sep = ""))## Saving 7 x 5 in image2.2 No clr, adjustment for oligodendrocytes
ctp_adjoligo.long %>% filter(!IID %in% asd_exclude$IID) %>% filter(!IID %in% scz_exclude$IID) %>%
mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = fct_rev(celltype), y = CTP, fill = dx)) +
geom_boxplot(position = position_dodge(.8),alpha=.8,outlier.size = 0)+
geom_point(aes(colour = dx), position = position_jitterdodge(.1),alpha=.3) +
coord_flip() + theme_bw() + xlab("") +
scale_fill_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
facet_grid(Level ~ study, scales = "free_y", space = 'free_y')
2.3 clr, no adjustment for oligodendrocytes
ctp.clr.1e3.long %>% filter(!IID %in% asd_exclude$IID) %>% filter(!IID %in% scz_exclude$IID) %>%
mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = fct_rev(celltype), y = CTP, fill = dx)) +
geom_boxplot(position = position_dodge(.8),alpha=.8,outlier.size = 0)+
geom_point(aes(colour = dx), position = position_jitterdodge(.1),alpha=.3) +
coord_flip() + theme_bw() + xlab("") +
scale_fill_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
facet_grid(Level ~ study, scales = "free_y", space = 'free_y')
ggsave(paste(out_dir, "/ctp_differences_bygroup_clr.png", sep = ""))## Saving 7 x 5 in imageggsave(paste(out_dir, "/ctp_differences_bygroup_clr.svg", sep = ""))## Saving 7 x 5 in image2.4 clr, adjustment for oligodendrocytes
ctp_adjoligo.clr.1e3.long %>% filter(!IID %in% asd_exclude$IID) %>% filter(!IID %in% scz_exclude$IID) %>%
mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = fct_rev(celltype), y = CTP, fill = dx)) +
geom_boxplot(position = position_dodge(.8),alpha=.8,outlier.size = 0)+
geom_point(aes(colour = dx), position = position_jitterdodge(.1),alpha=.3) +
coord_flip() + theme_bw() + xlab("") +
scale_fill_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
facet_grid(Level ~ study, scales = "free_y", space = 'free_y')
3 Age differences
- include all participants (ie. irrespective of within-study age matching)
3.1 Plot distributions
ctp.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>%
ggplot(aes(x = age, fill = dx)) +
geom_density(alpha = 0.5) +
scale_fill_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_wrap(~ study, scales = "free_y")## Warning: Removed 9 rows containing non-finite values (stat_density).
ggsave(paste(out_dir, "/demographic_differences_age.pdf", sep = ""), height = 3, width = 9)## Warning: Removed 9 rows containing non-finite values (stat_density).ggsave(paste(out_dir, "/demographic_differences_age.svg", sep = ""), height = 3, width = 9)## Warning: Removed 9 rows containing non-finite values (stat_density).ctp.long %>% filter(!IID %in% asd_exclude$IID) %>% filter(!IID %in% scz_exclude$IID) %>%
mutate(dx = fct_reorder(dx, MatchOrder)) %>%
ggplot(aes(x = age, fill = dx)) +
geom_density(alpha = 0.5) +
scale_fill_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_wrap(~ study, scales = "free_y")## Warning: Removed 9 rows containing non-finite values (stat_density).
ggsave(paste(out_dir, "/demographic_differences_age_asdsczexclude.pdf", sep = ""), height = 3, width = 9)## Warning: Removed 9 rows containing non-finite values (stat_density).ggsave(paste(out_dir, "/demographic_differences_age_asdsczexclude.svg", sep = ""), height = 3, width = 9)## Warning: Removed 9 rows containing non-finite values (stat_density).3.2 CTPs by age
3.2.1 No clr, no adjustment for oligodendrocytes
ctp.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = age, y = CTP, colour = dx)) +
geom_point(alpha = 0.5) +
geom_smooth() +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_grid(celltype ~ study, scales = "free_y")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 9 rows containing non-finite values (stat_smooth).## Warning: Removed 9 rows containing missing values (geom_point).
Keep CTP % constant
ctp.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = age, y = CTP, colour = dx)) +
geom_point(alpha = 0.5) +
geom_smooth() +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_grid(celltype ~ study, scales = "free_x")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 9 rows containing non-finite values (stat_smooth).## Warning: Removed 9 rows containing missing values (geom_point).
Aggregate into 1 plot
ctp.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = age, y = CTP)) +
geom_point(alpha = 0.6, aes(colour = dx, shape = study)) +
geom_smooth() +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_wrap(~ celltype, scales = "free_x", ncol = 1)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 9 rows containing non-finite values (stat_smooth).## Warning: Removed 9 rows containing missing values (geom_point).
ggsave(paste(out_dir, "/ctp_differences_byage_raw.png", sep = ""), height = 10, width = 3)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Removed 9 rows containing missing values (geom_point).ggsave(paste(out_dir, "/ctp_differences_byage_raw.svg", sep = ""), height = 10, width = 3)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Removed 9 rows containing missing values (geom_point).ctp.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = age, y = CTP)) +
geom_point(alpha = 0.6, aes(colour = dx, shape = study)) +
geom_smooth() +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_wrap(~ celltype, scales = "free_x", ncol = 3)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Removed 9 rows containing missing values (geom_point).
ggsave(paste(out_dir, "/ctp_differences_byage_raw_col3.png", sep = ""), height = 7, width = 7)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Removed 9 rows containing missing values (geom_point).ggsave(paste(out_dir, "/ctp_differences_byage_raw_col3.svg", sep = ""), height = 7, width = 7)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Removed 9 rows containing missing values (geom_point).3.2.2 No clr, adjustment for oligodendrocytes
ctp_adjoligo.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = age, y = CTP, colour = dx)) +
geom_point(alpha = 0.5) +
geom_smooth() +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_grid(celltype ~ study, scales = "free")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 8 rows containing non-finite values (stat_smooth).## Warning: Removed 8 rows containing missing values (geom_point).
3.3 clr, no adjustment for oligodendrocytes
ctp.clr.1e3.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = age, y = CTP, colour = dx)) +
geom_point(alpha = 0.5) +
geom_smooth() +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_grid(celltype ~ study, scales = "free")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
Plots that regress out batch effect
#batch_coef <- ctp.clr.1e3.long %>% group_by(study, celltype) %>%
batch_coef <- ctp.clr.1e3.long %>% group_by(celltype) %>%
do(model = tidy(lm(CTP ~ batch, data = .))) %>% unnest(model) %>%
mutate(term = gsub("batch", "", term)) %>%
dplyr::rename(batch = term) %>%
dplyr::select(c("celltype", "batch", "estimate"))
# Modify CTP in light of batch adjustment
# - subtraction as it's relative to a given batch (eg. coefficient +1, means that batch has +1 unit higher on average --> need to push back down)
ctp.clr.1e3.long %>%
#left_join(., batch_coef, by = c("study", "celltype", "batch")) %>%
left_join(., batch_coef, by = c("celltype", "batch")) %>%
mutate(CTP_batchadjust = ifelse(is.na(estimate), CTP, CTP - estimate)) %>%
mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = age, y = CTP_batchadjust, colour = dx)) +
geom_point(alpha = 0.5) +
geom_smooth() +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_grid(celltype ~ study)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
Keep CTP % constant
ctp.clr.1e3.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = age, y = CTP, colour = dx)) +
geom_point(alpha = 0.5) +
geom_smooth() +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_grid(celltype ~ study, scales = "free_x")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
Aggregate into 1 plot
ctp.clr.1e3.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = age, y = CTP)) +
geom_point(alpha = 0.6, aes(colour = dx, shape = study)) +
geom_smooth() +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
theme(legend.position = "none") +
facet_wrap(~ celltype, ncol = 1)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
ggsave(paste(out_dir, "/ctp_differences_byage_clr.png", sep = ""), height = 9, width = 3)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Removed 7 rows containing missing values (geom_point).ggsave(paste(out_dir, "/ctp_differences_byage_clr.svg", sep = ""), height = 9, width = 3)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Removed 7 rows containing missing values (geom_point).ctp.clr.1e3.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = age, y = CTP)) +
geom_point(alpha = 0.6, aes(colour = dx, shape = study)) +
geom_smooth() +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
theme(legend.position = "none") +
facet_wrap(~ celltype, ncol = 3)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Removed 7 rows containing missing values (geom_point).
ggsave(paste(out_dir, "/ctp_differences_byage_clr_col3.png", sep = ""), height = 7, width = 7)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Removed 7 rows containing missing values (geom_point).ggsave(paste(out_dir, "/ctp_differences_byage_clr_col3.svg", sep = ""), height = 7, width = 7)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Removed 7 rows containing missing values (geom_point).Aggregate into 1 plot, regressing out batch effects
# Change cell-type names
ctp.clr.1e3.long$celltype <- as.character(ctp.clr.1e3.long$celltype)
#ctp.clr.1e3.long$celltype <- gsub("NonN_", "", ctp.clr.1e3.long$celltype)
#ctp.clr.1e3.long$celltype <- gsub("_FGF3R", "", ctp.clr.1e3.long$celltype)
#ctp.clr.1e3.long$celltype <- gsub("_TYROBP", "", ctp.clr.1e3.long$celltype)
#ctp.clr.1e3.long$celltype <- gsub("_MBP", "", ctp.clr.1e3.long$celltype)
batch_coef2 <- batch_coef
batch_coef2$celltype <- as.character(batch_coef2$celltype)
#batch_coef2$celltype <- gsub("NonN_", "", batch_coef2$celltype)
#batch_coef2$celltype <- gsub("_FGF3R", "", batch_coef2$celltype)
#batch_coef2$celltype <- gsub("_TYROBP", "", batch_coef2$celltype)
#batch_coef2$celltype <- gsub("_MBP", "", batch_coef2$celltype)
ctp.clr.1e3.long %>%
# left_join(., batch_coef2, by = c("study", "celltype", "batch")) %>%
left_join(., batch_coef2, by = c("celltype", "batch")) %>%
mutate(CTP_batchadjust = ifelse(is.na(estimate), CTP, CTP - estimate)) %>%
mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = age, y = CTP_batchadjust)) +
geom_point(alpha = 0.6, aes(colour = dx, shape = study)) +
geom_smooth() +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
# theme(legend.position = "none") +
facet_wrap(~ celltype)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
# facet_wrap(~ celltype, ncol = 1)
ggsave(paste(out_dir, "/ctp_differences_byage_clr_adjustbatch.png", sep = ""), height = 5, width = 6)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Removed 7 rows containing missing values (geom_point).ggsave(paste(out_dir, "/ctp_differences_byage_clr_adjustbatch.svg", sep = ""), height = 6, width = 6)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Removed 7 rows containing missing values (geom_point).ctp.clr.1e3.long %>%
#left_join(., batch_coef2, by = c("study", "celltype", "batch")) %>%
left_join(., batch_coef2, by = c("celltype", "batch")) %>%
mutate(CTP_batchadjust = ifelse(is.na(estimate), CTP, CTP - estimate)) %>%
mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = age, y = CTP_batchadjust)) +
geom_point(alpha = 0.6, aes(colour = dx, shape = study)) +
geom_smooth() +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
theme(legend.position = "none") +
facet_wrap(~ celltype, ncol = 1)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 7 rows containing non-finite values (stat_smooth).## Warning: Removed 7 rows containing missing values (geom_point).
ggsave(paste(out_dir, "/ctp_differences_byage_clr_adjustbatch_1col.png", sep = ""), height = 9, width = 2)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Removed 7 rows containing missing values (geom_point).ggsave(paste(out_dir, "/ctp_differences_byage_clr_adjustbatch_1col.svg", sep = ""), height = 9, width = 2)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Removed 7 rows containing missing values (geom_point).3.4 clr, adjustment for oligodendrocytes
ctp_adjoligo.clr.1e3.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = age, y = CTP, colour = dx)) +
geom_point(alpha = 0.5) +
geom_smooth() +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_grid(celltype ~ study, scales = "free")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 6 rows containing non-finite values (stat_smooth).## Warning: Removed 6 rows containing missing values (geom_point).
4 Sex differences
4.1 Plot distributions
ctp %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>%
group_by(sex, dx, study) %>% dplyr::summarise(n = n()) %>%
filter(!is.na(sex)) %>%
ggplot(aes(x = sex, y = n, fill = dx)) +
geom_col() +
scale_fill_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_wrap(~ study, scales = "free_y")## `summarise()` has grouped output by 'sex', 'dx'. You can override using the
## `.groups` argument.
ggsave(paste(out_dir, "/demographic_differences_sex.svg", sep = ""), height = 3, width = 9)
ctp %>% filter(!IID %in% asd_exclude$IID) %>% filter(!IID %in% scz_exclude$IID) %>%
mutate(dx = fct_reorder(dx, MatchOrder)) %>%
group_by(sex, dx, study) %>% dplyr::summarise(n = n()) %>%
filter(!is.na(sex)) %>%
ggplot(aes(x = sex, y = n, fill = dx)) +
geom_col() +
scale_fill_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_wrap(~ study, scales = "free_y")## `summarise()` has grouped output by 'sex', 'dx'. You can override using the
## `.groups` argument.
ggsave(paste(out_dir, "/demographic_differences_sex_asdsczexclude.svg", sep = ""), height = 3, width = 9)4.2 No clr, no adjustment for oligodendrocytes
ctp.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>% filter(!is.na(sex)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = sex, y = CTP)) +
geom_violin() +
geom_point(aes(colour = dx)) +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_grid(celltype ~ study, scales = "free")
Keep CTP % constant
ctp.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>% filter(!is.na(sex)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = sex, y = CTP)) +
geom_violin() +
geom_point(aes(colour = dx)) +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_grid(celltype ~ study)
Aggregate into 1 plot
ctp.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>% filter(!is.na(sex)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = sex, y = CTP)) +
geom_violin() +
geom_boxplot(width=0.1, color="grey", alpha=0.2) +
geom_point(aes(colour = dx), position = position_jitterdodge(.1),alpha=.3) +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_wrap(~ celltype, ncol = 1)
ggsave(paste(out_dir, "/ctp_differences_bysex_raw.png", sep = ""), height = 9, width = 3)
ggsave(paste(out_dir, "/ctp_differences_bysex_raw.svg", sep = ""), height = 9, width = 3)4.3 No clr, adjustment for oligodendrocytes
ctp_adjoligo.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>% filter(!is.na(sex)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = sex, y = CTP)) +
geom_violin() +
geom_point(aes(colour = dx)) +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_grid(celltype ~ study, scales = "free")
4.4 clr, no adjustment for oligodendrocytes
ctp.clr.1e3.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>% filter(!is.na(sex)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = sex, y = CTP)) +
geom_violin() +
geom_point(aes(colour = dx)) +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_grid(celltype ~ study, scales = "free")
Keep CTP % constant
ctp.clr.1e3.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>% filter(!is.na(sex)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = sex, y = CTP)) +
geom_violin() +
geom_point(aes(colour = dx), position = position_jitterdodge(.1),alpha=.3) +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_grid(celltype ~ study)
Plots that regress out batch effect
ctp.clr.1e3.long %>%
#left_join(., batch_coef, by = c("study", "celltype", "batch")) %>%
left_join(., batch_coef, by = c("celltype", "batch")) %>%
mutate(CTP_batchadjust = ifelse(is.na(estimate), CTP, CTP - estimate)) %>%
mutate(dx = fct_reorder(dx, MatchOrder)) %>% filter(!is.na(sex)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = sex, y = CTP_batchadjust)) +
geom_violin() +
geom_point(aes(colour = dx), position = position_jitterdodge(.1),alpha=.3) +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_grid(celltype ~ study, scales = "free")
Aggregate into 1 plot
ctp.clr.1e3.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>% filter(!is.na(sex)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = sex, y = CTP)) +
geom_violin() +
geom_boxplot(width=0.1, color="grey", alpha=0.2) +
geom_point(aes(colour = dx), position = position_jitterdodge(.1),alpha=.3) +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
theme(legend.position = "none") +
facet_wrap(~ celltype, ncol = 1)
ggsave(paste(out_dir, "/ctp_differences_bysex_clr.png", sep = ""), height = 9, width = 3)
ggsave(paste(out_dir, "/ctp_differences_bysex_clr.svg", sep = ""), height = 9, width = 3)Aggregate into 1 plot, regressing out batch effects
ctp.clr.1e3.long %>%
#left_join(., batch_coef, by = c("study", "celltype", "batch")) %>%
left_join(., batch_coef, by = c("celltype", "batch")) %>%
mutate(CTP_batchadjust = ifelse(is.na(estimate), CTP, CTP - estimate)) %>%
mutate(dx = fct_reorder(dx, MatchOrder)) %>% filter(!is.na(sex)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = sex, y = CTP_batchadjust)) +
geom_violin() +
geom_boxplot(width=0.1, color="grey", alpha=0.2) +
geom_point(aes(colour = dx), position = position_jitterdodge(.1),alpha=.3) +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
theme(legend.position = "none") +
facet_wrap(~ celltype, ncol = 1)
ggsave(paste(out_dir, "/ctp_differences_bysex_clr_adjustbatch.png", sep = ""), height = 9, width = 3)
ggsave(paste(out_dir, "/ctp_differences_bysex_clr_adjustbatch.svg", sep = ""), height = 9, width = 3)4.5 clr, adjustment for oligodendrocytes
ctp_adjoligo.clr.1e3.long %>% mutate(dx = fct_reorder(dx, MatchOrder)) %>% filter(!is.na(sex)) %>%
mutate(celltype = fct_reorder(celltype, MatchCTP)) %>%
ggplot(aes(x = sex, y = CTP)) +
geom_violin() +
geom_point(aes(colour = dx)) +
scale_colour_manual(values = wes_palette(4, name = "Zissou1", type = "continuous"), name = "") +
theme_bw() +
facet_grid(celltype ~ study, scales = "free")
5 Variance in CTPs (clr-transformed) related to each of variables
exc_aov <- summary(aov(Exc ~ age + age2 + sex + batch + dx, data = ctp.clr.1e3))[[1]]
inh_aov <- summary(aov(Inh ~ age + age2 + sex + batch + dx, data = ctp.clr.1e3))[[1]]
astro_aov <- summary(aov(Astro ~ age + age2 + sex + batch + dx, data = ctp.clr.1e3))[[1]]
endo_aov <- summary(aov(Endo ~ age + age2 + sex + batch + dx, data = ctp.clr.1e3))[[1]]
micro_aov <- summary(aov(Micro ~ age + age2 + sex + batch + dx, data = ctp.clr.1e3))[[1]]
oligo_aov <- summary(aov(Oligo ~ age + age2 + sex + batch + dx, data = ctp.clr.1e3))[[1]]
opc_aov <- summary(aov(OPC ~ age + age2 + sex + batch + dx, data = ctp.clr.1e3))[[1]]
exc_aov <- summary(aov(Exc ~ age + age2 + sex + dx + batch, data = ctp.clr.1e3))[[1]]
inh_aov <- summary(aov(Inh ~ age + age2 + sex + dx + batch, data = ctp.clr.1e3))[[1]]
astro_aov <- summary(aov(Astro ~ age + age2 + sex + dx + batch, data = ctp.clr.1e3))[[1]]
endo_aov <- summary(aov(Endo ~ age + age2 + sex + dx + batch, data = ctp.clr.1e3))[[1]]
micro_aov <- summary(aov(Micro ~ age + age2 + sex + dx + batch, data = ctp.clr.1e3))[[1]]
oligo_aov <- summary(aov(Oligo ~ age + age2 + sex + dx + batch, data = ctp.clr.1e3))[[1]]
opc_aov <- summary(aov(OPC ~ age + age2 + sex + dx + batch, data = ctp.clr.1e3))[[1]]
# Summary tables
exc_sumsq <- exc_aov$'Sum Sq'[1:(nrow(exc_aov)-1)]
exc_sumsq <- exc_aov$'Sum Sq'
exc_totalsumsq <- sum(exc_aov$'Sum Sq')
exc_sumsq_summary <- data.frame(variable = rownames(exc_aov), SumSq = exc_sumsq, var_pc = exc_sumsq/exc_totalsumsq*100, Cumulative = cumsum(exc_sumsq), p = exc_aov$'Pr(>F)', CTP_PGS = "Exc")
exc_sumsq_summary## variable SumSq var_pc Cumulative p CTP_PGS
## 1 age 79.6076597 42.8573875 79.60766 2.990471e-182 Exc
## 2 age2 4.6961320 2.5281983 84.30379 2.170195e-16 Exc
## 3 sex 0.4159721 0.2239417 84.71976 1.334754e-02 Exc
## 4 dx 3.3115049 1.7827738 88.03127 2.161715e-10 Exc
## 5 batch 12.7613184 6.8701525 100.79259 7.457681e-34 Exc
## 6 Residuals 84.9575587 45.7375462 185.75015 NA Excinh_sumsq <- inh_aov$'Sum Sq'[1:(nrow(inh_aov)-1)]
inh_sumsq <- inh_aov$'Sum Sq'
inh_totalsumsq <- sum(inh_aov$'Sum Sq')
inh_sumsq_summary <- data.frame(variable = rownames(inh_aov), SumSq = inh_sumsq, var_pc = inh_sumsq/inh_totalsumsq*100, Cumulative = cumsum(inh_sumsq), p = inh_aov$'Pr(>F)', CTP_PGS = "Inh")
inh_sumsq_summary## variable SumSq var_pc Cumulative p CTP_PGS
## 1 age 9.21822219 24.54537318 9.218222 4.164646e-146 Inh
## 2 age2 6.53941357 17.41250570 15.757636 1.820376e-111 Inh
## 3 sex 0.01112518 0.02962304 15.768761 3.047738e-01 Inh
## 4 dx 3.28688914 8.75200434 19.055650 5.034134e-60 Inh
## 5 batch 5.26461884 14.01810799 24.320269 6.292427e-86 Inh
## 6 Residuals 13.23557559 35.24238575 37.555845 NA Inhastro_sumsq <- astro_aov$'Sum Sq'[1:(nrow(astro_aov)-1)]
astro_sumsq <- astro_aov$'Sum Sq'
astro_totalsumsq <- sum(astro_aov$'Sum Sq')
astro_sumsq_summary <- data.frame(variable = rownames(astro_aov), SumSq = astro_sumsq, var_pc = astro_sumsq/astro_totalsumsq*100, Cumulative = cumsum(astro_sumsq), p = astro_aov$'Pr(>F)', CTP_PGS = "Astro")
astro_sumsq_summary## variable SumSq var_pc Cumulative p CTP_PGS
## 1 age 17.85123148 27.76699763 17.85123 3.333018e-129 Astro
## 2 age2 2.44674736 3.80583424 20.29798 3.800389e-23 Astro
## 3 sex 0.01491579 0.02320101 20.31289 4.301207e-01 Astro
## 4 dx 3.51102523 5.46128313 23.82392 7.129611e-30 Astro
## 5 batch 10.43755423 16.23526892 34.26147 4.212740e-76 Astro
## 6 Residuals 30.02790902 46.70741508 64.28938 NA Astroendo_sumsq <- endo_aov$'Sum Sq'[1:(nrow(endo_aov)-1)]
endo_sumsq <- endo_aov$'Sum Sq'
endo_totalsumsq <- sum(endo_aov$'Sum Sq')
endo_sumsq_summary <- data.frame(variable = rownames(endo_aov), SumSq = endo_sumsq, var_pc = endo_sumsq/endo_totalsumsq*100, Cumulative = cumsum(endo_sumsq), p = endo_aov$'Pr(>F)', CTP_PGS = "Endo")
endo_sumsq_summary## variable SumSq var_pc Cumulative p CTP_PGS
## 1 age 12.6708741 48.2103905 12.67087 1.421522e-226 Endo
## 2 age2 0.5694232 2.1665527 13.24030 5.745377e-17 Endo
## 3 sex 0.1613513 0.6139125 13.40165 6.782471e-06 Endo
## 4 dx 1.1630014 4.4250105 14.56465 5.446448e-30 Endo
## 5 batch 1.8122031 6.8951061 16.37685 2.805202e-41 Endo
## 6 Residuals 9.9056017 37.6890277 26.28245 NA Endomicro_sumsq <- micro_aov$'Sum Sq'[1:(nrow(micro_aov)-1)]
micro_sumsq <- micro_aov$'Sum Sq'
micro_totalsumsq <- sum(micro_aov$'Sum Sq')
micro_sumsq_summary <- data.frame(variable = rownames(micro_aov), SumSq = micro_sumsq, var_pc = micro_sumsq/micro_totalsumsq*100, Cumulative = cumsum(micro_sumsq), p = micro_aov$'Pr(>F)', CTP_PGS = "Micro")
micro_sumsq_summary## variable SumSq var_pc Cumulative p CTP_PGS
## 1 age 47.276785441 58.505122328 47.27679 3.338662e-280 Micro
## 2 age2 0.005785524 0.007159598 47.28257 6.018253e-01 Micro
## 3 sex 0.375871823 0.465142179 47.65844 2.775322e-05 Micro
## 4 dx 2.918332023 3.611442073 50.57677 4.366453e-28 Micro
## 5 batch 3.596370455 4.450516072 54.17315 2.349713e-30 Micro
## 6 Residuals 26.634796943 32.960617749 80.80794 NA Microoligo_sumsq <- oligo_aov$'Sum Sq'[1:(nrow(oligo_aov)-1)]
oligo_sumsq <- oligo_aov$'Sum Sq'
oligo_totalsumsq <- sum(oligo_aov$'Sum Sq')
oligo_sumsq_summary <- data.frame(variable = rownames(oligo_aov), SumSq = oligo_sumsq, var_pc = oligo_sumsq/oligo_totalsumsq*100, Cumulative = cumsum(oligo_sumsq), p = oligo_aov$'Pr(>F)', CTP_PGS = "Oligo")
oligo_sumsq_summary## variable SumSq var_pc Cumulative p CTP_PGS
## 1 age 0.057303755 0.050045951 0.05730375 3.186402e-01 Oligo
## 2 age2 10.515589669 9.183738255 10.57289342 5.923015e-39 Oligo
## 3 sex 0.005682502 0.004962785 10.57857593 7.534480e-01 Oligo
## 4 dx 12.047922259 10.521993348 22.62649819 1.016036e-41 Oligo
## 5 batch 19.678349611 17.185989356 42.30484780 9.635563e-61 Oligo
## 6 Residuals 72.197431963 63.053270306 114.50227976 NA Oligoopc_sumsq <- opc_aov$'Sum Sq'[1:(nrow(opc_aov)-1)]
opc_sumsq <- opc_aov$'Sum Sq'
opc_totalsumsq <- sum(opc_aov$'Sum Sq')
opc_sumsq_summary <- data.frame(variable = rownames(opc_aov), SumSq = opc_sumsq, var_pc = opc_sumsq/opc_totalsumsq*100, Cumulative = cumsum(opc_sumsq), p = opc_aov$'Pr(>F)', CTP_PGS = "OPC")
opc_sumsq_summary## variable SumSq var_pc Cumulative p CTP_PGS
## 1 age 35.8500370 13.5576843 35.85004 1.719341e-77 OPC
## 2 age2 4.9064598 1.8555136 40.75650 2.470458e-13 OPC
## 3 sex 0.2762525 0.1044725 41.03275 7.931190e-02 OPC
## 4 dx 37.8852433 14.3273540 78.91799 1.033445e-78 OPC
## 5 batch 73.1781646 27.6743495 152.09616 6.398511e-131 OPC
## 6 Residuals 112.3298035 42.4806260 264.42596 NA OPCctp_aov_summary <- rbind(exc_sumsq_summary, inh_sumsq_summary, astro_sumsq_summary, endo_sumsq_summary, micro_sumsq_summary, oligo_sumsq_summary, opc_sumsq_summary)
ctp_aov_summary %>%
mutate(variable = gsub("[[:blank:]]", "", variable)) %>%
filter(variable != "Residuals") %>%
mutate(MatchVariable = match(variable, unique(variable))) %>%
mutate(MatchAnalysis = match(CTP_PGS, unique(CTP_PGS))) %>%
mutate(variable = fct_reorder(variable, rev(MatchVariable))) %>%
mutate(CTP_PGS = fct_reorder(CTP_PGS, rev(MatchAnalysis))) %>%
ggplot(aes(x = CTP_PGS, y = var_pc, fill = variable)) +
geom_bar(position="stack", stat="identity") +
scale_fill_brewer(palette = "Dark2") +
theme_bw() +
ylab("Variance % explained of clr-transformed CTP") + xlab("Cell-type") +
coord_flip()
ggsave(paste(out_dir, "/ctp_variance_covariates.png", sep = ""))## Saving 7 x 5 in imageggsave(paste(out_dir, "/ctp_variance_covariates.svg", sep = ""))## Saving 7 x 5 in image6 Overall contribution of CTPs to diagnosis
- obtain compositional PCs of CTP data
- likelihood ratio test to test for difference in model with and without CTP PCs
6.1 CTP PCs for all data
ctp.tmp <- as.matrix(ctp %>% dplyr::select(c(level2_cols)))## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(level2_cols)` instead of `level2_cols` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.rownames(ctp.tmp) <- ctp$IID
ctp.tmp[which(ctp.tmp <= 0)] <- 1e-3
ctp.acomp <- acomp(ctp.tmp)
rownames(ctp.acomp) <- ctp$IID
mean(ctp.acomp)## Exc Inh Astro Endo Micro Oligo
## "0.21784685" "0.18036249" "0.16943513" "0.03938087" "0.03325559" "0.34647048"
## OPC
## "0.01324857"
## attr(,"class")
## [1] "acomp"# Take supervised PCs, per individual
ctp.pcx <- princomp(ctp.acomp)
ctp.pcs <- ctp.pcx$scores
# Scree plot
screeplot(ctp.pcx, main = "Compositional PCA Scree Plot")
summary(ctp.pcx) # Take the PCs explaining 90-95% of variance## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 0.6441217 0.3434477 0.23876253 0.12365993 0.11286514
## Proportion of Variance 0.6629784 0.1884884 0.09109526 0.02443551 0.02035556
## Cumulative Proportion 0.6629784 0.8514668 0.94256207 0.96699758 0.98735315
## Comp.6
## Standard deviation 0.08896302
## Proportion of Variance 0.01264685
## Cumulative Proportion 1.00000000loadings(ctp.pcx)##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6
## Exc -0.536 0.388 0.301 0.535 0.192
## Inh -0.213 -0.602 0.438 -0.495
## Astro -0.310 -0.199 -0.421 -0.516 0.519
## Endo 0.121 -0.354 0.330 -0.518 -0.583
## Micro 0.303 -0.113 -0.583 0.222 0.482 0.362
## Oligo -0.824 0.416
## OPC 0.680 0.375 0.478 -0.113
##
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6
## SS loadings 1.000 1.000 1.000 1.000 1.000 1.000
## Proportion Var 0.143 0.143 0.143 0.143 0.143 0.143
## Cumulative Var 0.143 0.286 0.429 0.571 0.714 0.857loadings.ma <- loadings(ctp.pcx)
class(loadings.ma) <- "matrix"
loadings.long <- melt(data.frame(celltype = rownames(loadings.ma), loadings.ma), variable.name = "CTP_PC", value.name = "Loading")## Using celltype as id variablesanalysis_order <- c(level2_cols)
loadings.long$MatchAnalysis <- match(loadings.long$celltype, analysis_order)
loadings.long %>% mutate(celltype = fct_reorder(celltype, MatchAnalysis)) %>%
ggplot(aes(x = CTP_PC, y = Loading, fill = celltype)) +
geom_bar(stat="identity", position=position_dodge())
ggsave(paste(out_dir, "/ctp_noclr_pc_ROSMAP_ASDbrain_LIBD_loadings.svg", sep = ""))## Saving 7 x 5 in imageggsave(paste(out_dir, "/ctp_noclr_pc_ROSMAP_ASDbrain_LIBD_loadings.png", sep = ""))## Saving 7 x 5 in image# Take the PCs explaining 90-95% of variance
# - Check the % of variance explained by the PCs
# - from here, take the number of PCs that explain
foo <- ((ctp.pcx$sdev)^2)/((sum((ctp.pcx$sdev)^2)))*100
var_exp <- data.frame(Var_pc = foo, Cumulative_Var_pc = cumsum(foo))
print(var_exp)## Var_pc Cumulative_Var_pc
## Comp.1 66.297838 66.29784
## Comp.2 18.848843 85.14668
## Comp.3 9.109526 94.25621
## Comp.4 2.443551 96.69976
## Comp.5 2.035556 98.73531
## Comp.6 1.264685 100.00000# Choose the number of PCs to include as covariates
# - eg. in my dataset it's the first 3 PCs
pcs_keep <- rownames(var_exp)
#pcs_keep <- rownames(var_exp)[1:which(var_exp$Cumulative_Var_pc >= 95)[1]]
# Join into pheno data frame
ctp_pc <- inner_join(ctp, data.frame(IID = rownames(ctp.pcx$scores), ctp.pcx$scores[,pcs_keep]), by = "IID", type = "inner")
# Write tables for GWAS
saveRDS(ctp_pc, paste(out_dir, "/ctp_noclr_pc_ROSMAP_ASDbrain_LIBD.rds", sep = ""))
# Look for relationships
summary(lm(cbind(Comp.1, Comp.2, Comp.3, Comp.4, Comp.5, Comp.6) ~ age + age2 + sex + dx + batch, data = ctp_pc))## Response Comp.1 :
##
## Call:
## lm(formula = Comp.1 ~ age + age2 + sex + dx + batch, data = ctp_pc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2240 -0.1939 -0.0024 0.2037 1.8578
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.560e+00 7.952e-02 19.616 < 2e-16 ***
## age -6.567e-03 2.393e-03 -2.744 0.00615 **
## age2 6.508e-05 2.645e-05 2.460 0.01403 *
## sexM 3.025e-02 2.379e-02 1.272 0.20361
## dxAZD -1.242e-01 1.025e-01 -1.211 0.22597
## dxCTL -7.200e-02 9.767e-02 -0.737 0.46117
## dxSCZ -4.039e-02 1.093e-01 -0.370 0.71178
## batchASDbrain2 1.077e-01 1.548e-01 0.696 0.48659
## batchLieber_104 -1.095e+00 9.003e-02 -12.161 < 2e-16 ***
## batchLieber_244 -1.052e+00 7.666e-02 -13.724 < 2e-16 ***
## batchLieber_289 -1.115e+00 8.011e-02 -13.924 < 2e-16 ***
## batchLieber_30 -1.028e+00 1.118e-01 -9.192 < 2e-16 ***
## batchLieber_315 -1.037e+00 8.504e-02 -12.192 < 2e-16 ***
## batchROSMAP0 -2.028e+00 9.065e-02 -22.370 < 2e-16 ***
## batchROSMAP1 -1.601e+00 8.952e-02 -17.881 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3938 on 1254 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.6305, Adjusted R-squared: 0.6264
## F-statistic: 152.8 on 14 and 1254 DF, p-value: < 2.2e-16
##
##
## Response Comp.2 :
##
## Call:
## lm(formula = Comp.2 ~ age + age2 + sex + dx + batch, data = ctp_pc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.72839 -0.12905 0.01993 0.16720 0.85132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.166e-01 5.378e-02 13.325 < 2e-16 ***
## age -1.060e-02 1.618e-03 -6.553 8.21e-11 ***
## age2 9.292e-05 1.789e-05 5.194 2.40e-07 ***
## sexM -1.895e-02 1.609e-02 -1.178 0.2391
## dxAZD 7.023e-02 6.933e-02 1.013 0.3112
## dxCTL 5.643e-02 6.605e-02 0.854 0.3931
## dxSCZ 1.857e-01 7.391e-02 2.513 0.0121 *
## batchASDbrain2 -4.451e-02 1.047e-01 -0.425 0.6707
## batchLieber_104 -8.218e-01 6.088e-02 -13.498 < 2e-16 ***
## batchLieber_244 -8.369e-01 5.184e-02 -16.144 < 2e-16 ***
## batchLieber_289 -7.763e-01 5.417e-02 -14.329 < 2e-16 ***
## batchLieber_30 -9.118e-01 7.563e-02 -12.057 < 2e-16 ***
## batchLieber_315 -5.570e-01 5.751e-02 -9.685 < 2e-16 ***
## batchROSMAP0 -5.765e-01 6.131e-02 -9.403 < 2e-16 ***
## batchROSMAP1 -3.959e-01 6.054e-02 -6.540 8.93e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2663 on 1254 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.4047, Adjusted R-squared: 0.3981
## F-statistic: 60.9 on 14 and 1254 DF, p-value: < 2.2e-16
##
##
## Response Comp.3 :
##
## Call:
## lm(formula = Comp.3 ~ age + age2 + sex + dx + batch, data = ctp_pc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.96598 -0.11169 0.00549 0.11488 0.77336
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.360e-01 3.847e-02 -11.334 < 2e-16 ***
## age 1.310e-02 1.158e-03 11.316 < 2e-16 ***
## age2 -6.301e-05 1.280e-05 -4.923 9.66e-07 ***
## sexM -5.460e-03 1.151e-02 -0.474 0.635
## dxAZD -2.040e-02 4.960e-02 -0.411 0.681
## dxCTL -5.490e-02 4.725e-02 -1.162 0.245
## dxSCZ -5.229e-02 5.287e-02 -0.989 0.323
## batchASDbrain2 -7.760e-02 7.487e-02 -1.036 0.300
## batchLieber_104 4.069e-02 4.355e-02 0.934 0.350
## batchLieber_244 1.993e-02 3.709e-02 0.538 0.591
## batchLieber_289 -3.301e-02 3.875e-02 -0.852 0.395
## batchLieber_30 5.906e-02 5.410e-02 1.092 0.275
## batchLieber_315 -1.804e-02 4.114e-02 -0.439 0.661
## batchROSMAP0 -2.233e-01 4.386e-02 -5.092 4.09e-07 ***
## batchROSMAP1 -5.531e-02 4.331e-02 -1.277 0.202
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1905 on 1254 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.3708, Adjusted R-squared: 0.3637
## F-statistic: 52.78 on 14 and 1254 DF, p-value: < 2.2e-16
##
##
## Response Comp.4 :
##
## Call:
## lm(formula = Comp.4 ~ age + age2 + sex + dx + batch, data = ctp_pc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.73945 -0.04626 0.00912 0.06163 0.48182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.348e-02 2.201e-02 3.793 0.000156 ***
## age 5.255e-03 6.624e-04 7.934 4.68e-15 ***
## age2 -4.900e-05 7.323e-06 -6.692 3.31e-11 ***
## sexM -2.968e-02 6.584e-03 -4.508 7.17e-06 ***
## dxAZD 1.183e-03 2.838e-02 0.042 0.966747
## dxCTL -8.493e-05 2.704e-02 -0.003 0.997494
## dxSCZ -1.998e-02 3.025e-02 -0.660 0.509058
## batchASDbrain2 -7.113e-02 4.284e-02 -1.660 0.097069 .
## batchLieber_104 -1.487e-01 2.492e-02 -5.966 3.17e-09 ***
## batchLieber_244 -1.516e-01 2.122e-02 -7.146 1.51e-12 ***
## batchLieber_289 -1.272e-01 2.217e-02 -5.736 1.21e-08 ***
## batchLieber_30 -1.570e-01 3.096e-02 -5.070 4.57e-07 ***
## batchLieber_315 -1.623e-01 2.354e-02 -6.896 8.46e-12 ***
## batchROSMAP0 -1.597e-01 2.509e-02 -6.366 2.72e-10 ***
## batchROSMAP1 -2.114e-01 2.478e-02 -8.530 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.109 on 1254 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.232, Adjusted R-squared: 0.2235
## F-statistic: 27.06 on 14 and 1254 DF, p-value: < 2.2e-16
##
##
## Response Comp.5 :
##
## Call:
## lm(formula = Comp.5 ~ age + age2 + sex + dx + batch, data = ctp_pc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.47159 -0.05928 -0.00175 0.06252 0.40750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.702e-02 2.072e-02 3.717 0.000211 ***
## age -4.567e-03 6.236e-04 -7.324 4.30e-13 ***
## age2 2.924e-05 6.894e-06 4.241 2.39e-05 ***
## sexM 2.647e-02 6.198e-03 4.271 2.09e-05 ***
## dxAZD 2.313e-02 2.671e-02 0.866 0.386724
## dxCTL -1.308e-02 2.545e-02 -0.514 0.607310
## dxSCZ -4.283e-02 2.848e-02 -1.504 0.132887
## batchASDbrain2 -3.720e-02 4.033e-02 -0.922 0.356495
## batchLieber_104 4.350e-02 2.346e-02 1.854 0.063946 .
## batchLieber_244 6.134e-02 1.998e-02 3.071 0.002181 **
## batchLieber_289 3.138e-02 2.088e-02 1.503 0.133080
## batchLieber_30 7.585e-02 2.914e-02 2.603 0.009361 **
## batchLieber_315 -5.230e-03 2.216e-02 -0.236 0.813460
## batchROSMAP0 7.965e-02 2.362e-02 3.372 0.000770 ***
## batchROSMAP1 1.106e-01 2.333e-02 4.740 2.38e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1026 on 1254 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.183, Adjusted R-squared: 0.1738
## F-statistic: 20.06 on 14 and 1254 DF, p-value: < 2.2e-16
##
##
## Response Comp.6 :
##
## Call:
## lm(formula = Comp.6 ~ age + age2 + sex + dx + batch, data = ctp_pc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.37172 -0.05030 0.00504 0.05521 0.31257
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.421e-02 1.719e-02 -1.990 0.046861 *
## age 1.968e-03 5.174e-04 3.803 0.000150 ***
## age2 -1.472e-05 5.720e-06 -2.573 0.010205 *
## sexM 1.851e-02 5.143e-03 3.598 0.000333 ***
## dxAZD 1.023e-02 2.217e-02 0.462 0.644420
## dxCTL 4.804e-03 2.112e-02 0.227 0.820117
## dxSCZ -5.329e-03 2.363e-02 -0.226 0.821601
## batchASDbrain2 1.379e-02 3.346e-02 0.412 0.680299
## batchLieber_104 -4.633e-02 1.947e-02 -2.380 0.017461 *
## batchLieber_244 -5.134e-02 1.658e-02 -3.097 0.001998 **
## batchLieber_289 -7.197e-02 1.732e-02 -4.155 3.47e-05 ***
## batchLieber_30 -1.728e-02 2.418e-02 -0.714 0.475058
## batchLieber_315 -1.201e-02 1.839e-02 -0.653 0.513825
## batchROSMAP0 -7.058e-03 1.960e-02 -0.360 0.718864
## batchROSMAP1 -4.390e-02 1.936e-02 -2.268 0.023491 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08516 on 1254 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.09459, Adjusted R-squared: 0.08448
## F-statistic: 9.358 on 14 and 1254 DF, p-value: < 2.2e-166.2 ASDbrain
studyn <- "ASDbrain"
# Make compositional object
tmp <- ctp %>% filter(study == all_of(studyn)) %>% filter(!IID %in% asd_exclude$IID)
ctp.tmp <- as.matrix(tmp %>% dplyr::select(c(level2_cols)))
ctp.tmp[which(ctp.tmp <= 0)] <- 1e-3
ctp.acomp <- acomp(ctp.tmp)
rownames(ctp.acomp) <- tmp$IID
mean(ctp.acomp)## Exc Inh Astro Endo Micro Oligo
## "0.17507036" "0.16696920" "0.15284102" "0.06974837" "0.07261286" "0.30735471"
## OPC
## "0.05540349"
## attr(,"class")
## [1] "acomp"# Take supervised PCs, per individual
ctp.pcx <- princomp(ctp.acomp)
ctp.pcs <- ctp.pcx$scores
# Scree plot
screeplot(ctp.pcx, main = "Compositional PCA Scree Plot")
summary(ctp.pcx) # Take the PCs explaining 90-95% of variance## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 0.4370104 0.1877183 0.10987993 0.07224782 0.05279493
## Proportion of Variance 0.7723983 0.1425184 0.04883088 0.02111093 0.01127307
## Cumulative Proportion 0.7723983 0.9149167 0.96374759 0.98485852 0.99613159
## Comp.6
## Standard deviation 0.03092697
## Proportion of Variance 0.00386841
## Cumulative Proportion 1.00000000loadings(ctp.pcx)##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6
## Exc -0.484 0.683 -0.295 0.222 0.143
## Inh -0.215 -0.212 -0.334 -0.487 -0.646
## Astro -0.330 0.746 -0.352 0.228 0.119
## Endo -0.264 0.174 0.487 -0.149 -0.705
## Micro -0.433 -0.450 -0.132 0.664
## Oligo 0.753 0.448 -0.258 -0.125
## OPC 0.186 -0.167 0.518 -0.217 0.687
##
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6
## SS loadings 1.000 1.000 1.000 1.000 1.000 1.000
## Proportion Var 0.143 0.143 0.143 0.143 0.143 0.143
## Cumulative Var 0.143 0.286 0.429 0.571 0.714 0.857loadings.ma <- loadings(ctp.pcx)
class(loadings.ma) <- "matrix"
loadings.long <- melt(data.frame(celltype = rownames(loadings.ma), loadings.ma), variable.name = "CTP_PC", value.name = "Loading")## Using celltype as id variablesanalysis_order <- c(level2_cols)
loadings.long$MatchAnalysis <- match(loadings.long$celltype, analysis_order)
loadings.long %>% mutate(celltype = fct_reorder(celltype, MatchAnalysis)) %>%
ggplot(aes(x = CTP_PC, y = Loading, fill = celltype)) +
geom_bar(stat="identity", position=position_dodge())
ggsave(paste(out_dir, "/ctp_noclr_pc_ASDbrain_loadings.svg", sep = ""))## Saving 7 x 5 in imageggsave(paste(out_dir, "/ctp_noclr_pc_ASDbrain_loadings.png", sep = ""))## Saving 7 x 5 in image# Take the PCs explaining 90-95% of variance
# - Check the % of variance explained by the PCs
# - from here, take the number of PCs that explain
foo <- ((ctp.pcx$sdev)^2)/((sum((ctp.pcx$sdev)^2)))*100
var_exp <- data.frame(Var_pc = foo, Cumulative_Var_pc = cumsum(foo))
print(var_exp)## Var_pc Cumulative_Var_pc
## Comp.1 77.239830 77.23983
## Comp.2 14.251841 91.49167
## Comp.3 4.883088 96.37476
## Comp.4 2.111093 98.48585
## Comp.5 1.127307 99.61316
## Comp.6 0.386841 100.00000# Choose the number of PCs to include as covariates
# - eg. in my dataset it's the first 3 PCs
pcs_keep <- rownames(var_exp)[1:which(var_exp$Cumulative_Var_pc >= 95)[1]]
# Join into pheno data frame
ctp_pc <- inner_join(ctp, data.frame(IID = rownames(ctp.pcx$scores), ctp.pcx$scores[,pcs_keep]), by = "IID", type = "inner")
ctp_pc <- ctp_pc %>% mutate(MatchDx = match(dx, c("CTL", "ASD"))) %>% mutate(dx = fct_reorder(dx, MatchDx)) %>% dplyr::select(-c("MatchDx"))
# Write table
saveRDS(ctp_pc, paste(out_dir, "/ctp_noclr_pc95_", studyn, ".rds", sep = ""))
# Run model
mod0 <- lrm(dx ~ age + sex + batch, x = TRUE, y = TRUE, data = ctp_pc %>% filter(study == all_of(studyn)) %>% filter(!IID %in% asd_exclude$IID))
mod1 <- lrm(dx ~ age + sex + batch + Comp.1 + Comp.2 + Comp.3, x = TRUE, y = TRUE, data = ctp_pc %>% filter(study == all_of(studyn)) %>% filter(!IID %in% asd_exclude$IID))
mod0## Logistic Regression Model
##
## lrm(formula = dx ~ age + sex + batch, data = ctp_pc %>% filter(study ==
## all_of(studyn)) %>% filter(!IID %in% asd_exclude$IID), x = TRUE,
## y = TRUE)
##
## Model Likelihood Discrimination Rank Discrim.
## Ratio Test Indexes Indexes
## Obs 58 LR chi2 13.22 R2 0.272 C 0.722
## CTL 27 d.f. 3 R2(3,58) 0.161 Dxy 0.444
## ASD 31 Pr(> chi2) 0.0042 R2(3,43.3)0.210 gamma 0.449
## max |deriv| 0.09 Brier 0.204 tau-a 0.225
##
## Coef S.E. Wald Z Pr(>|Z|)
## Intercept 1.0364 0.9636 1.08 0.2821
## age -0.0336 0.0188 -1.79 0.0729
## sex=M 0.1721 0.6890 0.25 0.8027
## batch=ASDbrain2 8.4080 22.4842 0.37 0.7084
## mod1## Logistic Regression Model
##
## lrm(formula = dx ~ age + sex + batch + Comp.1 + Comp.2 + Comp.3,
## data = ctp_pc %>% filter(study == all_of(studyn)) %>% filter(!IID %in%
## asd_exclude$IID), x = TRUE, y = TRUE)
##
## Model Likelihood Discrimination Rank Discrim.
## Ratio Test Indexes Indexes
## Obs 58 LR chi2 19.49 R2 0.381 C 0.806
## CTL 27 d.f. 6 R2(6,58) 0.208 Dxy 0.613
## ASD 31 Pr(> chi2) 0.0034 R2(6,43.3)0.268 gamma 0.613
## max |deriv| 0.1 Brier 0.181 tau-a 0.310
##
## Coef S.E. Wald Z Pr(>|Z|)
## Intercept 0.8974 1.0929 0.82 0.4116
## age -0.0204 0.0217 -0.94 0.3486
## sex=M -0.2455 0.7444 -0.33 0.7416
## batch=ASDbrain2 8.3995 20.9311 0.40 0.6882
## Comp.1 0.4408 0.7729 0.57 0.5685
## Comp.2 -4.6111 1.9832 -2.33 0.0201
## Comp.3 -1.7074 3.1372 -0.54 0.5863
## lrtest(mod0, mod1)## Likelihood ratio test
##
## Model 1: dx ~ age + sex + batch
## Model 2: dx ~ age + sex + batch + Comp.1 + Comp.2 + Comp.3
## #Df LogLik Df Chisq Pr(>Chisq)
## 1 4 -33.457
## 2 7 -30.319 3 6.2763 0.09891 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1modx <- lrm(dx ~ Comp.1 + Comp.2 + Comp.3, x = TRUE, y = TRUE, data = ctp_pc %>% filter(study == all_of(studyn)) %>% filter(!IID %in% asd_exclude$IID))
modx## Logistic Regression Model
##
## lrm(formula = dx ~ Comp.1 + Comp.2 + Comp.3, data = ctp_pc %>%
## filter(study == all_of(studyn)) %>% filter(!IID %in% asd_exclude$IID),
## x = TRUE, y = TRUE)
##
## Model Likelihood Discrimination Rank Discrim.
## Ratio Test Indexes Indexes
## Obs 58 LR chi2 9.61 R2 0.204 C 0.704
## CTL 27 d.f. 3 R2(3,58) 0.108 Dxy 0.407
## ASD 31 Pr(> chi2) 0.0222 R2(3,43.3)0.142 gamma 0.407
## max |deriv| 2e-08 Brier 0.213 tau-a 0.206
##
## Coef S.E. Wald Z Pr(>|Z|)
## Intercept 0.1949 0.2902 0.67 0.5018
## Comp.1 0.4185 0.7071 0.59 0.5540
## Comp.2 -4.8998 1.8049 -2.71 0.0066
## Comp.3 -0.7211 2.6864 -0.27 0.7884
## saveRDS(mod0, paste(out_dir, "/ASD_methylation_brain_ctp_mod0.rds", sep = ""))
saveRDS(mod1, paste(out_dir, "/ASD_methylation_brain_ctp_mod1.rds", sep = ""))6.3 LIBD
studyn <- "LIBD"
# Make compositional object
tmp <- ctp %>% filter(study == all_of(studyn)) %>% filter(!IID %in% scz_exclude$IID)
ctp.tmp <- as.matrix(tmp %>% dplyr::select(c(level2_cols)))
ctp.tmp[which(ctp.tmp <= 0)] <- 1e-3
ctp.acomp <- acomp(ctp.tmp)
rownames(ctp.acomp) <- tmp$IID
mean(ctp.acomp)## Exc Inh Astro Endo Micro Oligo
## "0.17190648" "0.16256977" "0.15258062" "0.04341379" "0.03789097" "0.41669609"
## OPC
## "0.01494227"
## attr(,"class")
## [1] "acomp"# Take supervised PCs, per individual
ctp.pcx <- princomp(ctp.acomp)
ctp.pcs <- ctp.pcx$scores
# Scree plot
screeplot(ctp.pcx, main = "Compositional PCA Scree Plot")
summary(ctp.pcx) # Take the PCs explaining 90-95% of variance## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 0.474716 0.2368346 0.17049997 0.10769750 0.07567870
## Proportion of Variance 0.679646 0.1691629 0.08767256 0.03498052 0.01727278
## Cumulative Proportion 0.679646 0.8488090 0.93648152 0.97146204 0.98873482
## Comp.6
## Standard deviation 0.06111693
## Proportion of Variance 0.01126518
## Cumulative Proportion 1.00000000loadings(ctp.pcx)##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6
## Exc -0.703 0.473 -0.328 0.170
## Inh -0.121 -0.213 -0.317 -0.834
## Astro -0.268 -0.300 0.568 0.607
## Endo 0.188 -0.268 0.274 -0.666 0.480
## Micro 0.239 -0.482 -0.681 0.260 0.187
## Oligo 0.380 -0.774 0.286 0.171
## OPC 0.466 0.599 0.505 0.139
##
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6
## SS loadings 1.000 1.000 1.000 1.000 1.000 1.000
## Proportion Var 0.143 0.143 0.143 0.143 0.143 0.143
## Cumulative Var 0.143 0.286 0.429 0.571 0.714 0.857loadings.ma <- loadings(ctp.pcx)
class(loadings.ma) <- "matrix"
loadings.long <- melt(data.frame(celltype = rownames(loadings.ma), loadings.ma), variable.name = "CTP_PC", value.name = "Loading")## Using celltype as id variablesanalysis_order <- c(level2_cols)
loadings.long$MatchAnalysis <- match(loadings.long$celltype, analysis_order)
loadings.long %>% mutate(celltype = fct_reorder(celltype, MatchAnalysis)) %>%
ggplot(aes(x = CTP_PC, y = Loading, fill = celltype)) +
geom_bar(stat="identity", position=position_dodge())
ggsave(paste(out_dir, "/ctp_noclr_pc_LIBD_loadings.svg", sep = ""))## Saving 7 x 5 in imageggsave(paste(out_dir, "/ctp_noclr_pc_LIBD_loadings.png", sep = ""))## Saving 7 x 5 in image# Take the PCs explaining 90-95% of variance
# - Check the % of variance explained by the PCs
# - from here, take the number of PCs that explain
foo <- ((ctp.pcx$sdev)^2)/((sum((ctp.pcx$sdev)^2)))*100
var_exp <- data.frame(Var_pc = foo, Cumulative_Var_pc = cumsum(foo))
print(var_exp)## Var_pc Cumulative_Var_pc
## Comp.1 67.964602 67.96460
## Comp.2 16.916294 84.88090
## Comp.3 8.767256 93.64815
## Comp.4 3.498052 97.14620
## Comp.5 1.727278 98.87348
## Comp.6 1.126518 100.00000# Choose the number of PCs to include as covariates
# - eg. in my dataset it's the first 3 PCs
pcs_keep <- rownames(var_exp)[1:which(var_exp$Cumulative_Var_pc >= 95)[1]]
# Join into pheno data frame
ctp_pc <- inner_join(ctp, data.frame(IID = rownames(ctp.pcx$scores), ctp.pcx$scores[,pcs_keep]), by = "IID", type = "inner")
ctp_pc <- ctp_pc %>% mutate(MatchDx = match(dx, c("CTL", "SCZ"))) %>% mutate(dx = fct_reorder(dx, MatchDx)) %>% dplyr::select(-c("MatchDx"))
# Write table
saveRDS(ctp_pc, paste(out_dir, "/ctp_noclr_pc95_", studyn, ".rds", sep = ""))
# Run model
mod0 <- lrm(dx ~ age + sex + batch, x = TRUE, y = TRUE, data = ctp_pc %>% filter(study == all_of(studyn)) %>% filter(!IID %in% scz_exclude$IID))
mod1 <- lrm(dx ~ age + sex + batch + Comp.1 + Comp.2 + Comp.3 + Comp.4, x = TRUE, y = TRUE, data = ctp_pc %>% filter(study == all_of(studyn)) %>% filter(!IID %in% scz_exclude$IID))
mod0## Logistic Regression Model
##
## lrm(formula = dx ~ age + sex + batch, data = ctp_pc %>% filter(study ==
## all_of(studyn)) %>% filter(!IID %in% scz_exclude$IID), x = TRUE,
## y = TRUE)
##
## Model Likelihood Discrimination Rank Discrim.
## Ratio Test Indexes Indexes
## Obs 402 LR chi2 112.53 R2 0.326 C 0.755
## CTL 217 d.f. 6 R2(6,402)0.233 Dxy 0.511
## SCZ 185 Pr(> chi2) <0.0001 R2(6,299.6)0.299 gamma 0.511
## max |deriv| 0.04 Brier 0.194 tau-a 0.254
##
## Coef S.E. Wald Z Pr(>|Z|)
## Intercept -12.1495 27.5881 -0.44 0.6597
## age 0.0389 0.0084 4.63 <0.0001
## sex=M -0.5285 0.2443 -2.16 0.0305
## batch=Lieber_244 10.6725 27.5840 0.39 0.6988
## batch=Lieber_289 10.3718 27.5840 0.38 0.7069
## batch=Lieber_30 21.0306 41.8385 0.50 0.6152
## batch=Lieber_315 10.4588 27.5845 0.38 0.7046
## mod1## Logistic Regression Model
##
## lrm(formula = dx ~ age + sex + batch + Comp.1 + Comp.2 + Comp.3 +
## Comp.4, data = ctp_pc %>% filter(study == all_of(studyn)) %>%
## filter(!IID %in% scz_exclude$IID), x = TRUE, y = TRUE)
##
## Model Likelihood Discrimination Rank Discrim.
## Ratio Test Indexes Indexes
## Obs 402 LR chi2 136.35 R2 0.384 C 0.800
## CTL 217 d.f. 10 R2(10,402)0.270 Dxy 0.600
## SCZ 185 Pr(> chi2) <0.0001 R2(10,299.6)0.344 gamma 0.600
## max |deriv| 0.04 Brier 0.179 tau-a 0.299
##
## Coef S.E. Wald Z Pr(>|Z|)
## Intercept -11.6673 27.0907 -0.43 0.6667
## age 0.0295 0.0110 2.68 0.0073
## sex=M -0.4127 0.2603 -1.59 0.1128
## batch=Lieber_244 10.6149 27.0855 0.39 0.6951
## batch=Lieber_289 10.3106 27.0855 0.38 0.7034
## batch=Lieber_30 21.0400 41.1842 0.51 0.6094
## batch=Lieber_315 9.8542 27.0862 0.36 0.7160
## Comp.1 -0.2531 0.2578 -0.98 0.3263
## Comp.2 2.3650 0.5585 4.23 <0.0001
## Comp.3 1.1899 0.8619 1.38 0.1674
## Comp.4 2.0001 1.3121 1.52 0.1274
## lrtest(mod0, mod1)## Likelihood ratio test
##
## Model 1: dx ~ age + sex + batch
## Model 2: dx ~ age + sex + batch + Comp.1 + Comp.2 + Comp.3 + Comp.4
## #Df LogLik Df Chisq Pr(>Chisq)
## 1 7 -221.11
## 2 11 -209.19 4 23.826 8.654e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1modx <- lrm(dx ~ Comp.1 + Comp.2 + Comp.3 + Comp.4, x = TRUE, y = TRUE, data = ctp_pc %>% filter(study == all_of(studyn)) %>% filter(!IID %in% scz_exclude$IID))
modx## Logistic Regression Model
##
## lrm(formula = dx ~ Comp.1 + Comp.2 + Comp.3 + Comp.4, data = ctp_pc %>%
## filter(study == all_of(studyn)) %>% filter(!IID %in% scz_exclude$IID),
## x = TRUE, y = TRUE)
##
## Model Likelihood Discrimination Rank Discrim.
## Ratio Test Indexes Indexes
## Obs 402 LR chi2 37.10 R2 0.118 C 0.672
## CTL 217 d.f. 4 R2(4,402)0.079 Dxy 0.345
## SCZ 185 Pr(> chi2) <0.0001 R2(4,299.6)0.105 gamma 0.345
## max |deriv| 3e-11 Brier 0.226 tau-a 0.172
##
## Coef S.E. Wald Z Pr(>|Z|)
## Intercept -0.1733 0.1050 -1.65 0.0989
## Comp.1 -0.0861 0.2253 -0.38 0.7024
## Comp.2 2.1638 0.4877 4.44 <0.0001
## Comp.3 1.7716 0.6363 2.78 0.0054
## Comp.4 2.7102 1.0117 2.68 0.0074
## saveRDS(mod0, paste(out_dir, "/LIBD_ctp_mod0.rds", sep = ""))
saveRDS(mod1, paste(out_dir, "/LIBD_ctp_mod1.rds", sep = ""))6.4 ROSMAP
studyn <- "ROSMAP"
# Make compositional object
tmp <- ctp %>% filter(study == all_of(studyn))
ctp.tmp <- as.matrix(tmp %>% dplyr::select(c(level2_cols)))
ctp.tmp[which(ctp.tmp <= 0)] <- 1e-3
ctp.acomp <- acomp(ctp.tmp)
rownames(ctp.acomp) <- tmp$IID
mean(ctp.acomp)## Exc Inh Astro Endo Micro Oligo
## "0.25556872" "0.18533533" "0.17725115" "0.03342661" "0.02644621" "0.31168476"
## OPC
## "0.01028722"
## attr(,"class")
## [1] "acomp"# Take supervised PCs, per individual
ctp.pcx <- princomp(ctp.acomp)
ctp.pcs <- ctp.pcx$scores
# Scree plot
screeplot(ctp.pcx, main = "Compositional PCA Scree Plot")
summary(ctp.pcx) # Take the PCs explaining 90-95% of variance## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 0.4794320 0.2177051 0.1910892 0.11050329 0.09158798
## Proportion of Variance 0.6747181 0.1391251 0.1071866 0.03584419 0.02462324
## Cumulative Proportion 0.6747181 0.8138432 0.9210298 0.95687401 0.98149725
## Comp.6
## Standard deviation 0.07939332
## Proportion of Variance 0.01850275
## Cumulative Proportion 1.00000000loadings(ctp.pcx)##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6
## Exc -0.329 -0.153 0.590 -0.299 0.536
## Inh -0.119 -0.145 0.168 -0.401 -0.154 -0.781
## Astro -0.294 -0.284 0.609 0.541 -0.152
## Endo -0.162 -0.200 0.384 -0.789 0.136
## Micro -0.179 -0.705 -0.463 0.208 0.247
## Oligo -0.175 0.903
## OPC 0.866 0.280 0.119 0.119
##
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6
## SS loadings 1.000 1.000 1.000 1.000 1.000 1.000
## Proportion Var 0.143 0.143 0.143 0.143 0.143 0.143
## Cumulative Var 0.143 0.286 0.429 0.571 0.714 0.857loadings.ma <- loadings(ctp.pcx)
class(loadings.ma) <- "matrix"
loadings.long <- melt(data.frame(celltype = rownames(loadings.ma), loadings.ma), variable.name = "CTP_PC", value.name = "Loading")## Using celltype as id variablesanalysis_order <- c(level2_cols)
loadings.long$MatchAnalysis <- match(loadings.long$celltype, analysis_order)
loadings.long %>% mutate(celltype = fct_reorder(celltype, MatchAnalysis)) %>%
ggplot(aes(x = CTP_PC, y = Loading, fill = celltype)) +
geom_bar(stat="identity", position=position_dodge())
ggsave(paste(out_dir, "/ctp_noclr_pc_ROSMAP_loadings.svg", sep = ""))## Saving 7 x 5 in imageggsave(paste(out_dir, "/ctp_noclr_pc_ROSMAP_loadings.png", sep = ""))## Saving 7 x 5 in image# Take the PCs explaining 90-95% of variance
# - Check the % of variance explained by the PCs
# - from here, take the number of PCs that explain
foo <- ((ctp.pcx$sdev)^2)/((sum((ctp.pcx$sdev)^2)))*100
var_exp <- data.frame(Var_pc = foo, Cumulative_Var_pc = cumsum(foo))
print(var_exp)## Var_pc Cumulative_Var_pc
## Comp.1 67.471811 67.47181
## Comp.2 13.912510 81.38432
## Comp.3 10.718660 92.10298
## Comp.4 3.584419 95.68740
## Comp.5 2.462324 98.14973
## Comp.6 1.850275 100.00000# Choose the number of PCs to include as covariates
# - eg. in my dataset it's the first 3 PCs
pcs_keep <- rownames(var_exp)[1:which(var_exp$Cumulative_Var_pc >= 95)[1]]
# Join into pheno data frame
ctp_pc <- inner_join(ctp, data.frame(IID = rownames(ctp.pcx$scores), ctp.pcx$scores[,pcs_keep]), by = "IID", type = "inner")
ctp_pc <- ctp_pc %>% mutate(MatchDx = match(dx, c("CTL", "AZD"))) %>% mutate(dx = fct_reorder(dx, MatchDx)) %>% dplyr::select(-c("MatchDx"))
# Write table
saveRDS(ctp_pc, paste(out_dir, "/ctp_noclr_pc95_", studyn, ".rds", sep = ""))
# Run model
mod0 <- lrm(dx ~ age + sex + batch, x = TRUE, y = TRUE, data = ctp_pc %>% filter(study == all_of(studyn)))
mod1 <- lrm(dx ~ age + sex + batch + Comp.1 + Comp.2 + Comp.3 + Comp.4, x = TRUE, y = TRUE, data = ctp_pc %>% filter(study == all_of(studyn)))
mod0## Frequencies of Missing Values Due to Each Variable
## dx age sex batch
## 0 1 1 0
##
## Logistic Regression Model
##
## lrm(formula = dx ~ age + sex + batch, data = ctp_pc %>% filter(study ==
## all_of(studyn)), x = TRUE, y = TRUE)
##
##
## Model Likelihood Discrimination Rank Discrim.
## Ratio Test Indexes Indexes
## Obs 718 LR chi2 62.29 R2 0.112 C 0.664
## CTL 418 d.f. 3 R2(3,718)0.079 Dxy 0.328
## AZD 300 Pr(> chi2) <0.0001 R2(3,524)0.107 gamma 0.346
## max |deriv| 9e-06 Brier 0.223 tau-a 0.160
##
## Coef S.E. Wald Z Pr(>|Z|)
## Intercept -12.8460 1.8394 -6.98 <0.0001
## age 0.1423 0.0209 6.81 <0.0001
## sex=M 0.0060 0.1681 0.04 0.9717
## batch=ROSMAP1 0.2693 0.1643 1.64 0.1011
## mod1## Frequencies of Missing Values Due to Each Variable
## dx age sex batch Comp.1 Comp.2 Comp.3 Comp.4
## 0 1 1 0 0 0 0 0
##
## Logistic Regression Model
##
## lrm(formula = dx ~ age + sex + batch + Comp.1 + Comp.2 + Comp.3 +
## Comp.4, data = ctp_pc %>% filter(study == all_of(studyn)),
## x = TRUE, y = TRUE)
##
##
## Model Likelihood Discrimination Rank Discrim.
## Ratio Test Indexes Indexes
## Obs 718 LR chi2 90.00 R2 0.159 C 0.704
## CTL 418 d.f. 7 R2(7,718)0.109 Dxy 0.408
## AZD 300 Pr(> chi2) <0.0001 R2(7,524)0.147 gamma 0.408
## max |deriv| 2e-05 Brier 0.214 tau-a 0.199
##
## Coef S.E. Wald Z Pr(>|Z|)
## Intercept -12.2339 1.8740 -6.53 <0.0001
## age 0.1356 0.0212 6.39 <0.0001
## sex=M 0.0367 0.1727 0.21 0.8317
## batch=ROSMAP1 0.1828 0.1994 0.92 0.3594
## Comp.1 -0.0772 0.2021 -0.38 0.7024
## Comp.2 -0.0442 0.3713 -0.12 0.9053
## Comp.3 1.3743 0.4456 3.08 0.0020
## Comp.4 -3.0507 0.7471 -4.08 <0.0001
## lrtest(mod0, mod1)## Likelihood ratio test
##
## Model 1: dx ~ age + sex + batch
## Model 2: dx ~ age + sex + batch + Comp.1 + Comp.2 + Comp.3 + Comp.4
## #Df LogLik Df Chisq Pr(>Chisq)
## 1 4 -456.80
## 2 8 -442.94 4 27.717 1.423e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1modx <- lrm(dx ~ Comp.1 + Comp.2 + Comp.3 + Comp.4, x = TRUE, y = TRUE, data = ctp_pc %>% filter(study == all_of(studyn)))
modx## Logistic Regression Model
##
## lrm(formula = dx ~ Comp.1 + Comp.2 + Comp.3 + Comp.4, data = ctp_pc %>%
## filter(study == all_of(studyn)), x = TRUE, y = TRUE)
##
## Model Likelihood Discrimination Rank Discrim.
## Ratio Test Indexes Indexes
## Obs 719 LR chi2 38.73 R2 0.071 C 0.631
## CTL 419 d.f. 4 R2(4,719)0.047 Dxy 0.262
## AZD 300 Pr(> chi2) <0.0001 R2(4,524.5)0.064 gamma 0.263
## max |deriv| 5e-15 Brier 0.230 tau-a 0.128
##
## Coef S.E. Wald Z Pr(>|Z|)
## Intercept -0.3483 0.0778 -4.48 <0.0001
## Comp.1 0.0626 0.1672 0.37 0.7082
## Comp.2 -0.0529 0.3605 -0.15 0.8833
## Comp.3 1.8008 0.4221 4.27 <0.0001
## Comp.4 -3.1047 0.7203 -4.31 <0.0001
## saveRDS(mod0, paste(out_dir, "/ROSMAP_ctp_mod0.rds", sep = ""))
saveRDS(mod1, paste(out_dir, "/ROSMAP_ctp_mod1.rds", sep = ""))7 Linear models for individual cell-type differences
7.1 Sub-types
7.1.1 clr, no adjustment for oligodendrocytes
7.1.1.1 ASDbrain
summary(lm(cbind(Exc, Inh, Astro, Endo, Micro, Oligo, OPC) ~ dx, data = ctp.clr.1e3 %>% filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID)))## Response Exc :
##
## Call:
## lm(formula = Exc ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.79517 -0.10083 0.05935 0.14285 0.32690
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.31174 0.04397 7.090 2.46e-09 ***
## dxCTL 0.11939 0.06444 1.853 0.0692 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2448 on 56 degrees of freedom
## Multiple R-squared: 0.05775, Adjusted R-squared: 0.04092
## F-statistic: 3.432 on 1 and 56 DF, p-value: 0.06921
##
##
## Response Inh :
##
## Call:
## lm(formula = Inh ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.230859 -0.094632 -0.002926 0.105418 0.223843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.32569 0.02151 15.139 <2e-16 ***
## dxCTL -0.01236 0.03153 -0.392 0.697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1198 on 56 degrees of freedom
## Multiple R-squared: 0.002735, Adjusted R-squared: -0.01507
## F-statistic: 0.1536 on 1 and 56 DF, p-value: 0.6966
##
##
## Response Astro :
##
## Call:
## lm(formula = Astro ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.49194 -0.09152 0.02459 0.13428 0.29991
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.227494 0.030523 7.453 6.17e-10 ***
## dxCTL 0.008671 0.044736 0.194 0.847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1699 on 56 degrees of freedom
## Multiple R-squared: 0.0006704, Adjusted R-squared: -0.01717
## F-statistic: 0.03757 on 1 and 56 DF, p-value: 0.847
##
##
## Response Endo :
##
## Call:
## lm(formula = Endo ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12401 -0.03931 -0.00047 0.03740 0.21080
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.54316 0.01215 -44.700 <2e-16 ***
## dxCTL -0.02108 0.01781 -1.184 0.242
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06766 on 56 degrees of freedom
## Multiple R-squared: 0.0244, Adjusted R-squared: 0.006983
## F-statistic: 1.401 on 1 and 56 DF, p-value: 0.2416
##
##
## Response Micro :
##
## Call:
## lm(formula = Micro ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.24394 -0.07252 -0.02098 0.06577 0.25309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.47378 0.01840 -25.747 < 2e-16 ***
## dxCTL -0.08367 0.02697 -3.102 0.00301 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1025 on 56 degrees of freedom
## Multiple R-squared: 0.1467, Adjusted R-squared: 0.1314
## F-statistic: 9.624 on 1 and 56 DF, p-value: 0.003007
##
##
## Response Oligo :
##
## Call:
## lm(formula = Oligo ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.59523 -0.22961 -0.08931 0.21070 0.78627
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.91840 0.06165 14.897 <2e-16 ***
## dxCTL 0.02520 0.09035 0.279 0.781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3432 on 56 degrees of freedom
## Multiple R-squared: 0.001387, Adjusted R-squared: -0.01644
## F-statistic: 0.0778 on 1 and 56 DF, p-value: 0.7813
##
##
## Response OPC :
##
## Call:
## lm(formula = OPC ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.16502 -0.05990 -0.02423 0.04531 0.29068
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.76639 0.01748 -43.849 <2e-16 ***
## dxCTL -0.03616 0.02562 -1.412 0.164
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09731 on 56 degrees of freedom
## Multiple R-squared: 0.03436, Adjusted R-squared: 0.01711
## F-statistic: 1.992 on 1 and 56 DF, p-value: 0.1636summary(lm(cbind(Exc, Inh, Astro, Endo, Micro, Oligo, OPC) ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>% filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID)))## Response Exc :
##
## Call:
## lm(formula = Exc ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.75304 -0.07787 0.05203 0.15367 0.36890
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1006606 0.2461906 0.409 0.684
## dxCTL 0.1073554 0.0701213 1.531 0.132
## age 0.0187009 0.0124363 1.504 0.139
## age2 -0.0002351 0.0001525 -1.542 0.129
## sexM -0.1110047 0.0775934 -1.431 0.159
## batchASDbrain2 -0.0866973 0.1047283 -0.828 0.412
##
## Residual standard error: 0.2407 on 52 degrees of freedom
## Multiple R-squared: 0.1545, Adjusted R-squared: 0.07319
## F-statistic: 1.9 on 5 and 52 DF, p-value: 0.1102
##
##
## Response Inh :
##
## Call:
## lm(formula = Inh ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23036 -0.09011 -0.01411 0.09828 0.25067
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.352e-01 1.244e-01 2.694 0.00947 **
## dxCTL -1.260e-02 3.543e-02 -0.356 0.72357
## age -1.054e-03 6.284e-03 -0.168 0.86746
## age2 3.147e-06 7.708e-05 0.041 0.96759
## sexM 3.917e-02 3.921e-02 0.999 0.32242
## batchASDbrain2 -3.709e-02 5.292e-02 -0.701 0.48645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1216 on 52 degrees of freedom
## Multiple R-squared: 0.04569, Adjusted R-squared: -0.04607
## F-statistic: 0.4979 on 5 and 52 DF, p-value: 0.7763
##
##
## Response Astro :
##
## Call:
## lm(formula = Astro ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.49340 -0.09444 0.04083 0.12648 0.25435
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.039e-01 1.784e-01 1.142 0.258
## dxCTL 2.129e-03 5.083e-02 0.042 0.967
## age 2.438e-03 9.014e-03 0.270 0.788
## age2 -1.959e-05 1.106e-04 -0.177 0.860
## sexM -4.415e-02 5.624e-02 -0.785 0.436
## batchASDbrain2 6.332e-03 7.591e-02 0.083 0.934
##
## Residual standard error: 0.1744 on 52 degrees of freedom
## Multiple R-squared: 0.0224, Adjusted R-squared: -0.0716
## F-statistic: 0.2383 on 5 and 52 DF, p-value: 0.9437
##
##
## Response Endo :
##
## Call:
## lm(formula = Endo ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.110949 -0.046803 0.000079 0.026488 0.230405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.391e-01 6.898e-02 -6.365 5.08e-08 ***
## dxCTL -1.515e-02 1.965e-02 -0.771 0.444
## age -5.264e-03 3.485e-03 -1.510 0.137
## age2 5.386e-05 4.274e-05 1.260 0.213
## sexM 2.177e-03 2.174e-02 0.100 0.921
## batchASDbrain2 9.592e-03 2.934e-02 0.327 0.745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06743 on 52 degrees of freedom
## Multiple R-squared: 0.1001, Adjusted R-squared: 0.01359
## F-statistic: 1.157 on 5 and 52 DF, p-value: 0.3428
##
##
## Response Micro :
##
## Call:
## lm(formula = Micro ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.242322 -0.058188 -0.002788 0.055054 0.267380
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.473e-01 9.416e-02 -2.626 0.0113 *
## dxCTL -6.358e-02 2.682e-02 -2.371 0.0215 *
## age -1.167e-02 4.756e-03 -2.454 0.0175 *
## age2 1.115e-04 5.834e-05 1.911 0.0616 .
## sexM 2.363e-02 2.968e-02 0.796 0.4295
## batchASDbrain2 2.418e-02 4.005e-02 0.604 0.5486
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09204 on 52 degrees of freedom
## Multiple R-squared: 0.3605, Adjusted R-squared: 0.2991
## F-statistic: 5.864 on 5 and 52 DF, p-value: 0.000228
##
##
## Response Oligo :
##
## Call:
## lm(formula = Oligo ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.54230 -0.24729 -0.08104 0.17814 0.81947
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.833e-01 3.583e-01 2.186 0.0333 *
## dxCTL 1.866e-02 1.021e-01 0.183 0.8556
## age 1.269e-04 1.810e-02 0.007 0.9944
## age2 3.754e-05 2.220e-04 0.169 0.8664
## sexM 7.835e-02 1.129e-01 0.694 0.4909
## batchASDbrain2 5.747e-02 1.524e-01 0.377 0.7077
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3502 on 52 degrees of freedom
## Multiple R-squared: 0.0345, Adjusted R-squared: -0.05834
## F-statistic: 0.3716 on 5 and 52 DF, p-value: 0.8658
##
##
## Response OPC :
##
## Call:
## lm(formula = OPC ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.20572 -0.05836 -0.01291 0.05016 0.28057
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.366e-01 1.016e-01 -7.250 1.98e-09 ***
## dxCTL -3.681e-02 2.894e-02 -1.272 0.209
## age -3.277e-03 5.133e-03 -0.638 0.526
## age2 4.871e-05 6.296e-05 0.774 0.443
## sexM 1.184e-02 3.202e-02 0.370 0.713
## batchASDbrain2 2.622e-02 4.322e-02 0.607 0.547
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09932 on 52 degrees of freedom
## Multiple R-squared: 0.06595, Adjusted R-squared: -0.02386
## F-statistic: 0.7343 on 5 and 52 DF, p-value: 0.6017.1.1.2 LIBD
summary(lm(cbind(Exc, Inh, Astro, Endo, Micro, Oligo, OPC) ~ dx, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID)))## Response Exc :
##
## Call:
## lm(formula = Exc ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.85572 -0.08622 0.05281 0.17875 0.63801
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.65077 0.02348 27.711 <2e-16 ***
## dxSCZ 0.02033 0.03462 0.587 0.557
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3459 on 400 degrees of freedom
## Multiple R-squared: 0.0008616, Adjusted R-squared: -0.001636
## F-statistic: 0.345 on 1 and 400 DF, p-value: 0.5573
##
##
## Response Inh :
##
## Call:
## lm(formula = Inh ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.259420 -0.054351 0.003769 0.052136 0.252121
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.605642 0.006008 100.802 <2e-16 ***
## dxSCZ -0.002959 0.008857 -0.334 0.738
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08851 on 400 degrees of freedom
## Multiple R-squared: 0.000279, Adjusted R-squared: -0.00222
## F-statistic: 0.1116 on 1 and 400 DF, p-value: 0.7385
##
##
## Response Astro :
##
## Call:
## lm(formula = Astro ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.52208 -0.08499 0.00482 0.09322 0.51153
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.53231 0.01070 49.758 <2e-16 ***
## dxSCZ 0.01860 0.01577 1.179 0.239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1576 on 400 degrees of freedom
## Multiple R-squared: 0.003465, Adjusted R-squared: 0.0009738
## F-statistic: 1.391 on 1 and 400 DF, p-value: 0.239
##
##
## Response Endo :
##
## Call:
## lm(formula = Endo ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.21324 -0.05900 -0.01539 0.04361 0.44679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.718328 0.006215 -115.57 <2e-16 ***
## dxSCZ 0.004948 0.009162 0.54 0.589
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09156 on 400 degrees of freedom
## Multiple R-squared: 0.0007287, Adjusted R-squared: -0.00177
## F-statistic: 0.2917 on 1 and 400 DF, p-value: 0.5895
##
##
## Response Micro :
##
## Call:
## lm(formula = Micro ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.47252 -0.09892 -0.00463 0.07610 0.60643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.82969 0.01075 -77.195 < 2e-16 ***
## dxSCZ -0.04873 0.01584 -3.075 0.00225 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1583 on 400 degrees of freedom
## Multiple R-squared: 0.0231, Adjusted R-squared: 0.02066
## F-statistic: 9.459 on 1 and 400 DF, p-value: 0.002246
##
##
## Response Oligo :
##
## Call:
## lm(formula = Oligo ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.83510 -0.15758 -0.00938 0.15574 0.91863
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.57915 0.01766 89.399 < 2e-16 ***
## dxSCZ -0.07306 0.02604 -2.806 0.00526 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2602 on 400 degrees of freedom
## Multiple R-squared: 0.0193, Adjusted R-squared: 0.01685
## F-statistic: 7.874 on 1 and 400 DF, p-value: 0.005262
##
##
## Response OPC :
##
## Call:
## lm(formula = OPC ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.36419 -0.14716 0.00457 0.13405 0.89596
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.81985 0.01861 -97.790 < 2e-16 ***
## dxSCZ 0.08087 0.02743 2.948 0.00339 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2741 on 400 degrees of freedom
## Multiple R-squared: 0.02126, Adjusted R-squared: 0.01882
## F-statistic: 8.691 on 1 and 400 DF, p-value: 0.003386summary(lm(cbind(Exc, Inh, Astro, Endo, Micro, Oligo, OPC) ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID)))## Response Exc :
##
## Call:
## lm(formula = Exc ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.89166 -0.09513 0.05729 0.19200 0.60517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.556e-01 1.438e-01 3.168 0.00166 **
## dxSCZ 2.288e-02 3.881e-02 0.590 0.55577
## age 9.737e-03 5.428e-03 1.794 0.07360 .
## age2 -8.868e-05 5.529e-05 -1.604 0.10952
## sexM -8.203e-03 3.746e-02 -0.219 0.82677
## batchLieber_244 -7.559e-02 7.028e-02 -1.075 0.28281
## batchLieber_289 -1.586e-02 7.126e-02 -0.223 0.82403
## batchLieber_30 -1.037e-01 1.001e-01 -1.036 0.30083
## batchLieber_315 8.656e-03 7.762e-02 0.112 0.91126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3447 on 393 degrees of freedom
## Multiple R-squared: 0.02524, Adjusted R-squared: 0.0054
## F-statistic: 1.272 on 8 and 393 DF, p-value: 0.2564
##
##
## Response Inh :
##
## Call:
## lm(formula = Inh ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.220296 -0.054465 0.000495 0.054463 0.230232
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.029e-01 3.565e-02 16.912 <2e-16 ***
## dxSCZ 5.504e-03 9.621e-03 0.572 0.5676
## age 5.937e-04 1.346e-03 0.441 0.6593
## age2 -1.573e-05 1.371e-05 -1.147 0.2519
## sexM 9.236e-03 9.286e-03 0.995 0.3206
## batchLieber_244 1.610e-02 1.742e-02 0.924 0.3561
## batchLieber_289 1.142e-02 1.767e-02 0.647 0.5183
## batchLieber_30 -1.586e-02 2.480e-02 -0.639 0.5229
## batchLieber_315 -3.839e-02 1.924e-02 -1.995 0.0467 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08546 on 393 degrees of freedom
## Multiple R-squared: 0.08422, Adjusted R-squared: 0.06558
## F-statistic: 4.518 on 8 and 393 DF, p-value: 2.861e-05
##
##
## Response Astro :
##
## Call:
## lm(formula = Astro ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.48662 -0.08547 0.01077 0.09506 0.50878
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.929e-01 6.464e-02 7.625 1.85e-13 ***
## dxSCZ 2.162e-02 1.745e-02 1.239 0.2160
## age 2.947e-03 2.440e-03 1.208 0.2278
## age2 -3.570e-05 2.485e-05 -1.436 0.1517
## sexM -2.300e-02 1.684e-02 -1.366 0.1727
## batchLieber_244 -2.219e-02 3.159e-02 -0.702 0.4829
## batchLieber_289 1.247e-02 3.203e-02 0.389 0.6973
## batchLieber_30 -3.146e-02 4.498e-02 -0.699 0.4847
## batchLieber_315 6.515e-02 3.489e-02 1.867 0.0626 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.155 on 393 degrees of freedom
## Multiple R-squared: 0.05329, Adjusted R-squared: 0.03402
## F-statistic: 2.765 on 8 and 393 DF, p-value: 0.005536
##
##
## Response Endo :
##
## Call:
## lm(formula = Endo ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.24321 -0.05275 -0.00806 0.04276 0.44940
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.284e-01 3.558e-02 -17.665 < 2e-16 ***
## dxSCZ 1.565e-02 9.601e-03 1.630 0.103876
## age -2.784e-03 1.343e-03 -2.073 0.038822 *
## age2 1.044e-05 1.368e-05 0.763 0.445679
## sexM -2.729e-02 9.267e-03 -2.945 0.003426 **
## batchLieber_244 1.509e-02 1.739e-02 0.868 0.385892
## batchLieber_289 6.195e-02 1.763e-02 3.514 0.000492 ***
## batchLieber_30 -2.139e-02 2.475e-02 -0.864 0.388037
## batchLieber_315 4.435e-02 1.920e-02 2.309 0.021447 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08528 on 393 degrees of freedom
## Multiple R-squared: 0.1482, Adjusted R-squared: 0.1309
## F-statistic: 8.548 on 8 and 393 DF, p-value: 9.473e-11
##
##
## Response Micro :
##
## Call:
## lm(formula = Micro ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.39012 -0.07686 -0.02074 0.06882 0.57314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.756e-01 5.833e-02 -9.868 < 2e-16 ***
## dxSCZ -2.616e-02 1.574e-02 -1.662 0.0974 .
## age -9.533e-03 2.202e-03 -4.330 1.9e-05 ***
## age2 5.201e-05 2.243e-05 2.319 0.0209 *
## sexM 2.482e-02 1.519e-02 1.634 0.1031
## batchLieber_244 4.813e-02 2.851e-02 1.688 0.0922 .
## batchLieber_289 3.899e-02 2.891e-02 1.349 0.1781
## batchLieber_30 6.268e-02 4.058e-02 1.544 0.1233
## batchLieber_315 3.401e-02 3.149e-02 1.080 0.2807
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1398 on 393 degrees of freedom
## Multiple R-squared: 0.2514, Adjusted R-squared: 0.2361
## F-statistic: 16.5 on 8 and 393 DF, p-value: < 2.2e-16
##
##
## Response Oligo :
##
## Call:
## lm(formula = Oligo ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.76499 -0.16800 0.00177 0.14269 0.90854
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.498e+00 1.024e-01 14.624 < 2e-16 ***
## dxSCZ -9.644e-02 2.765e-02 -3.488 0.000542 ***
## age 2.850e-03 3.867e-03 0.737 0.461515
## age2 5.617e-06 3.939e-05 0.143 0.886683
## sexM 2.242e-02 2.669e-02 0.840 0.401412
## batchLieber_244 -3.302e-02 5.007e-02 -0.659 0.510038
## batchLieber_289 -8.538e-02 5.077e-02 -1.682 0.093417 .
## batchLieber_30 5.682e-02 7.128e-02 0.797 0.425814
## batchLieber_315 -2.445e-01 5.530e-02 -4.421 1.27e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2456 on 393 degrees of freedom
## Multiple R-squared: 0.1417, Adjusted R-squared: 0.1242
## F-statistic: 8.11 on 8 and 393 DF, p-value: 3.717e-10
##
##
## Response OPC :
##
## Call:
## lm(formula = OPC ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.37313 -0.14144 -0.00242 0.14589 0.87972
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.846e+00 1.120e-01 -16.471 <2e-16 ***
## dxSCZ 5.694e-02 3.024e-02 1.883 0.0604 .
## age -3.811e-03 4.229e-03 -0.901 0.3681
## age2 7.204e-05 4.308e-05 1.672 0.0953 .
## sexM 2.019e-03 2.919e-02 0.069 0.9449
## batchLieber_244 5.147e-02 5.476e-02 0.940 0.3478
## batchLieber_289 -2.360e-02 5.553e-02 -0.425 0.6710
## batchLieber_30 5.286e-02 7.796e-02 0.678 0.4981
## batchLieber_315 1.307e-01 6.048e-02 2.161 0.0313 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2686 on 393 degrees of freedom
## Multiple R-squared: 0.07684, Adjusted R-squared: 0.05805
## F-statistic: 4.089 on 8 and 393 DF, p-value: 0.0001072Check confounding of batch for Exc and Oligo
## Check per-batch effect for Exc and Oligo
summary(lm(Micro ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_289")))##
## Call:
## lm(formula = Micro ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch == "Lieber_289"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.39670 -0.07215 0.00550 0.06004 0.53388
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.328e-01 5.567e-02 -9.571 3.77e-16 ***
## dxSCZ 1.114e-04 2.564e-02 0.004 0.99654
## age -1.002e-02 2.166e-03 -4.626 1.02e-05 ***
## age2 6.073e-05 2.176e-05 2.790 0.00621 **
## sexM 5.529e-04 2.496e-02 0.022 0.98237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1258 on 110 degrees of freedom
## Multiple R-squared: 0.3358, Adjusted R-squared: 0.3117
## F-statistic: 13.9 on 4 and 110 DF, p-value: 3.276e-09summary(lm(Micro ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_315")))##
## Call:
## lm(formula = Micro ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch == "Lieber_315"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.25066 -0.06464 -0.00835 0.06299 0.41720
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.435e-01 5.027e-02 -12.800 < 2e-16 ***
## dxSCZ 4.778e-03 3.301e-02 0.145 0.88534
## age -7.058e-03 2.327e-03 -3.033 0.00344 **
## age2 2.982e-05 3.171e-05 0.940 0.35039
## sexM 6.238e-02 3.296e-02 1.893 0.06271 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1198 on 67 degrees of freedom
## Multiple R-squared: 0.4637, Adjusted R-squared: 0.4317
## F-statistic: 14.48 on 4 and 67 DF, p-value: 1.421e-08print("N.B. Lieber_104 has CONTROLS ONLY")## [1] "N.B. Lieber_104 has CONTROLS ONLY"summary(lm(Micro ~ age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_104")))##
## Call:
## lm(formula = Micro ~ age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch == "Lieber_104"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.41544 -0.06913 0.00189 0.06950 0.44253
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.711e-01 5.488e-02 -8.584 5.95e-11 ***
## age -1.117e-02 3.263e-03 -3.423 0.00135 **
## age2 6.016e-05 4.239e-05 1.419 0.16288
## sexM -6.356e-02 4.335e-02 -1.466 0.14973
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1493 on 44 degrees of freedom
## Multiple R-squared: 0.5886, Adjusted R-squared: 0.5605
## F-statistic: 20.98 on 3 and 44 DF, p-value: 1.372e-08print("N.B. Lieber_30 has SCHIZOPHRENIA PATIENTS ONLY")## [1] "N.B. Lieber_30 has SCHIZOPHRENIA PATIENTS ONLY"summary(lm(Micro ~ age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_30")))##
## Call:
## lm(formula = Micro ~ age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch == "Lieber_30"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.17713 -0.07667 -0.05847 0.04542 0.43153
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.6735907 0.3952967 -1.704 0.102
## age 0.0027987 0.0172763 0.162 0.873
## age2 -0.0001306 0.0001841 -0.710 0.485
## sexM -0.0004509 0.0717097 -0.006 0.995
##
## Residual standard error: 0.1586 on 22 degrees of freedom
## Multiple R-squared: 0.3834, Adjusted R-squared: 0.2993
## F-statistic: 4.559 on 3 and 22 DF, p-value: 0.01248summary(lm(Micro ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_244")))##
## Call:
## lm(formula = Micro ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch == "Lieber_244"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.29803 -0.06965 -0.01315 0.05126 0.60667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.249e-01 3.578e-02 -14.668 < 2e-16 ***
## dxSCZ -5.587e-02 2.113e-02 -2.645 0.0088 **
## age -9.420e-03 2.033e-03 -4.634 6.31e-06 ***
## age2 4.782e-05 2.807e-05 1.704 0.0899 .
## sexM 4.416e-02 2.075e-02 2.128 0.0345 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1341 on 209 degrees of freedom
## Multiple R-squared: 0.4798, Adjusted R-squared: 0.4698
## F-statistic: 48.19 on 4 and 209 DF, p-value: < 2.2e-16summary(lm(Micro ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289", "Lieber_244", "Lieber_315"))))##
## Call:
## lm(formula = Micro ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289",
## "Lieber_244", "Lieber_315")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.39166 -0.07472 -0.01655 0.06350 0.56942
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.280e-01 2.497e-02 -21.142 < 2e-16 ***
## dxSCZ -2.908e-02 1.457e-02 -1.995 0.0467 *
## age -9.682e-03 1.125e-03 -8.604 < 2e-16 ***
## age2 5.650e-05 1.372e-05 4.120 4.63e-05 ***
## sexM 2.833e-02 1.425e-02 1.988 0.0475 *
## batchLieber_289 -1.395e-02 1.583e-02 -0.881 0.3787
## batchLieber_315 -2.622e-02 1.787e-02 -1.468 0.1430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1299 on 394 degrees of freedom
## Multiple R-squared: 0.4525, Adjusted R-squared: 0.4442
## F-statistic: 54.28 on 6 and 394 DF, p-value: < 2.2e-16summary(lm(Micro ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289", "Lieber_244", "Lieber_315", "Lieber_30"))))##
## Call:
## lm(formula = Micro ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289",
## "Lieber_244", "Lieber_315", "Lieber_30")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.39230 -0.07362 -0.01567 0.06141 0.57427
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.292e-01 2.518e-02 -21.018 < 2e-16 ***
## dxSCZ -2.703e-02 1.477e-02 -1.830 0.067927 .
## age -9.485e-03 1.139e-03 -8.328 1.18e-15 ***
## age2 5.213e-05 1.382e-05 3.773 0.000184 ***
## sexM 2.897e-02 1.405e-02 2.062 0.039798 *
## batchLieber_289 -1.166e-02 1.605e-02 -0.726 0.468092
## batchLieber_30 1.178e-02 2.876e-02 0.409 0.682422
## batchLieber_315 -2.509e-02 1.815e-02 -1.382 0.167639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1321 on 419 degrees of freedom
## Multiple R-squared: 0.4411, Adjusted R-squared: 0.4317
## F-statistic: 47.24 on 7 and 419 DF, p-value: < 2.2e-16summary(lm(Micro ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289", "Lieber_244", "Lieber_315", "Lieber_30", "Lieber_104"))))##
## Call:
## lm(formula = Micro ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289",
## "Lieber_244", "Lieber_315", "Lieber_30", "Lieber_104")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.39040 -0.07488 -0.01407 0.06228 0.56968
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.477e-01 2.704e-02 -20.255 < 2e-16 ***
## dxSCZ -2.514e-02 1.477e-02 -1.701 0.0895 .
## age -9.866e-03 1.071e-03 -9.210 < 2e-16 ***
## age2 5.427e-05 1.316e-05 4.123 4.42e-05 ***
## sexM 1.775e-02 1.341e-02 1.324 0.1861
## batchLieber_244 3.640e-02 2.278e-02 1.598 0.1108
## batchLieber_289 2.585e-02 2.416e-02 1.070 0.2853
## batchLieber_30 4.897e-02 3.613e-02 1.355 0.1760
## batchLieber_315 1.164e-02 2.595e-02 0.448 0.6540
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1343 on 466 degrees of freedom
## Multiple R-squared: 0.4566, Adjusted R-squared: 0.4473
## F-statistic: 48.94 on 8 and 466 DF, p-value: < 2.2e-16summary(lm(Micro ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289", "Lieber_244", "Lieber_315", "Lieber_30", "Lieber_104"))))##
## Call:
## lm(formula = Micro ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289",
## "Lieber_244", "Lieber_315", "Lieber_30", "Lieber_104")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.41186 -0.07334 -0.01357 0.06290 0.57462
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.271e-01 2.221e-02 -23.727 < 2e-16 ***
## dxSCZ -1.690e-02 1.364e-02 -1.240 0.215696
## age -9.640e-03 1.063e-03 -9.069 < 2e-16 ***
## age2 5.018e-05 1.293e-05 3.880 0.000119 ***
## sexM 2.140e-02 1.323e-02 1.618 0.106399
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1343 on 470 degrees of freedom
## Multiple R-squared: 0.4519, Adjusted R-squared: 0.4472
## F-statistic: 96.87 on 4 and 470 DF, p-value: < 2.2e-16summary(lm(Oligo ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_289")))##
## Call:
## lm(formula = Oligo ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch == "Lieber_289"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.47414 -0.14351 -0.02345 0.12643 0.84804
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.716e-01 1.083e-01 8.974 8.72e-15 ***
## dxSCZ -1.034e-02 4.986e-02 -0.207 0.83611
## age 1.817e-02 4.213e-03 4.313 3.52e-05 ***
## age2 -1.332e-04 4.232e-05 -3.146 0.00213 **
## sexM 4.765e-02 4.855e-02 0.982 0.32845
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2446 on 110 degrees of freedom
## Multiple R-squared: 0.216, Adjusted R-squared: 0.1874
## F-statistic: 7.575 on 4 and 110 DF, p-value: 1.988e-05summary(lm(Oligo ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_315")))##
## Call:
## lm(formula = Oligo ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch == "Lieber_315"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.70115 -0.10991 0.00296 0.15360 0.48191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.708e-01 1.021e-01 8.531 2.65e-12 ***
## dxSCZ -1.460e-01 6.702e-02 -2.179 0.032884 *
## age 1.803e-02 4.724e-03 3.816 0.000298 ***
## age2 -1.242e-04 6.439e-05 -1.929 0.057950 .
## sexM 1.209e-02 6.692e-02 0.181 0.857207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2432 on 67 degrees of freedom
## Multiple R-squared: 0.3783, Adjusted R-squared: 0.3412
## F-statistic: 10.19 on 4 and 67 DF, p-value: 1.662e-06print("N.B. Lieber_104 has CONTROLS ONLY")## [1] "N.B. Lieber_104 has CONTROLS ONLY"summary(lm(Oligo ~ age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_104")))##
## Call:
## lm(formula = Oligo ~ age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch == "Lieber_104"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.63239 -0.19849 -0.00609 0.11907 1.07282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.174e+00 1.146e-01 10.248 3.13e-13 ***
## age 1.548e-02 6.812e-03 2.273 0.028 *
## age2 -9.495e-05 8.849e-05 -1.073 0.289
## sexM -4.269e-02 9.050e-02 -0.472 0.639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3116 on 44 degrees of freedom
## Multiple R-squared: 0.3465, Adjusted R-squared: 0.302
## F-statistic: 7.777 on 3 and 44 DF, p-value: 0.0002838print("N.B. Lieber_30 has SCHIZOPHRENIA PATIENTS ONLY")## [1] "N.B. Lieber_30 has SCHIZOPHRENIA PATIENTS ONLY"summary(lm(Oligo ~ age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_30")))##
## Call:
## lm(formula = Oligo ~ age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch == "Lieber_30"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.29493 -0.15638 -0.00970 0.08445 0.56092
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.487e+00 5.511e-01 2.698 0.0131 *
## age 7.076e-03 2.409e-02 0.294 0.7717
## age2 -9.693e-05 2.567e-04 -0.378 0.7093
## sexM 3.905e-02 9.998e-02 0.391 0.6998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2212 on 22 degrees of freedom
## Multiple R-squared: 0.03444, Adjusted R-squared: -0.09723
## F-statistic: 0.2616 on 3 and 22 DF, p-value: 0.8523summary(lm(Oligo ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_244")))##
## Call:
## lm(formula = Oligo ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch == "Lieber_244"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.84089 -0.21869 -0.00294 0.16638 0.83055
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.186e+00 7.438e-02 15.945 < 2e-16 ***
## dxSCZ -1.471e-01 4.392e-02 -3.349 0.000962 ***
## age 1.978e-02 4.225e-03 4.682 5.1e-06 ***
## age2 -1.958e-04 5.835e-05 -3.356 0.000940 ***
## sexM 4.404e-03 4.314e-02 0.102 0.918770
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2788 on 209 degrees of freedom
## Multiple R-squared: 0.1608, Adjusted R-squared: 0.1447
## F-statistic: 10.01 on 4 and 209 DF, p-value: 1.981e-07summary(lm(Oligo ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289", "Lieber_244", "Lieber_315"))))##
## Call:
## lm(formula = Oligo ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289",
## "Lieber_244", "Lieber_315")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.81279 -0.18552 -0.00944 0.15322 0.89923
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.177e+00 5.095e-02 23.089 < 2e-16 ***
## dxSCZ -1.103e-01 2.973e-02 -3.710 0.000237 ***
## age 1.623e-02 2.296e-03 7.068 7.19e-12 ***
## age2 -1.248e-04 2.798e-05 -4.460 1.07e-05 ***
## sexM 1.572e-02 2.908e-02 0.540 0.589200
## batchLieber_289 -6.742e-02 3.229e-02 -2.088 0.037474 *
## batchLieber_315 -2.450e-01 3.646e-02 -6.720 6.37e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2651 on 394 degrees of freedom
## Multiple R-squared: 0.2497, Adjusted R-squared: 0.2382
## F-statistic: 21.85 on 6 and 394 DF, p-value: < 2.2e-16summary(lm(Oligo ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289", "Lieber_244", "Lieber_315", "Lieber_30"))))##
## Call:
## lm(formula = Oligo ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289",
## "Lieber_244", "Lieber_315", "Lieber_30")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.80811 -0.18776 -0.00251 0.14508 0.90303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.175e+00 5.010e-02 23.453 < 2e-16 ***
## dxSCZ -1.068e-01 2.939e-02 -3.634 0.000313 ***
## age 1.634e-02 2.266e-03 7.208 2.66e-12 ***
## age2 -1.289e-04 2.749e-05 -4.687 3.75e-06 ***
## sexM 1.988e-02 2.795e-02 0.711 0.477451
## batchLieber_289 -6.412e-02 3.194e-02 -2.007 0.045340 *
## batchLieber_30 7.544e-02 5.722e-02 1.318 0.188100
## batchLieber_315 -2.433e-01 3.612e-02 -6.737 5.37e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2628 on 419 degrees of freedom
## Multiple R-squared: 0.247, Adjusted R-squared: 0.2344
## F-statistic: 19.63 on 7 and 419 DF, p-value: < 2.2e-16summary(lm(Oligo ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289", "Lieber_244", "Lieber_315", "Lieber_30", "Lieber_104"))))##
## Call:
## lm(formula = Oligo ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289",
## "Lieber_244", "Lieber_315", "Lieber_30", "Lieber_104")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.81720 -0.18558 -0.00756 0.14663 1.06419
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.175e+00 5.387e-02 21.821 < 2e-16 ***
## dxSCZ -1.127e-01 2.943e-02 -3.828 0.000147 ***
## age 1.644e-02 2.134e-03 7.703 8.06e-14 ***
## age2 -1.259e-04 2.622e-05 -4.800 2.14e-06 ***
## sexM 1.266e-02 2.671e-02 0.474 0.635830
## batchLieber_244 -1.688e-03 4.538e-02 -0.037 0.970349
## batchLieber_289 -7.069e-02 4.813e-02 -1.469 0.142591
## batchLieber_30 7.424e-02 7.198e-02 1.031 0.302892
## batchLieber_315 -2.475e-01 5.169e-02 -4.787 2.28e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2675 on 466 degrees of freedom
## Multiple R-squared: 0.2603, Adjusted R-squared: 0.2476
## F-statistic: 20.5 on 8 and 466 DF, p-value: < 2.2e-16summary(lm(Oligo ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289", "Lieber_244", "Lieber_315", "Lieber_30", "Lieber_104"))))##
## Call:
## lm(formula = Oligo ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289",
## "Lieber_244", "Lieber_315", "Lieber_30", "Lieber_104")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8684 -0.1949 -0.0080 0.1660 1.1093
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.130e+00 4.652e-02 24.286 < 2e-16 ***
## dxSCZ -1.017e-01 2.855e-02 -3.562 0.000405 ***
## age 1.662e-02 2.226e-03 7.466 4.06e-13 ***
## age2 -1.346e-04 2.708e-05 -4.970 9.41e-07 ***
## sexM 1.390e-02 2.770e-02 0.502 0.616045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2812 on 470 degrees of freedom
## Multiple R-squared: 0.1755, Adjusted R-squared: 0.1685
## F-statistic: 25.01 on 4 and 470 DF, p-value: < 2.2e-16summary(lm(OPC ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_289")))##
## Call:
## lm(formula = OPC ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch == "Lieber_289"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.30576 -0.11601 0.01649 0.16040 0.73460
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.805e+00 1.227e-01 -14.702 <2e-16 ***
## dxSCZ -3.669e-02 5.653e-02 -0.649 0.5177
## age -2.984e-03 4.776e-03 -0.625 0.5334
## age2 7.436e-05 4.798e-05 1.550 0.1241
## sexM -1.044e-01 5.503e-02 -1.898 0.0604 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2773 on 110 degrees of freedom
## Multiple R-squared: 0.126, Adjusted R-squared: 0.09423
## F-statistic: 3.965 on 4 and 110 DF, p-value: 0.00481summary(lm(OPC ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_315")))##
## Call:
## lm(formula = OPC ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch == "Lieber_315"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45403 -0.15057 -0.02209 0.15028 0.80844
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.704e+00 9.037e-02 -18.859 <2e-16 ***
## dxSCZ 3.976e-02 5.933e-02 0.670 0.505
## age -4.509e-03 4.183e-03 -1.078 0.285
## age2 7.761e-05 5.701e-05 1.361 0.178
## sexM 2.934e-02 5.925e-02 0.495 0.622
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2153 on 67 degrees of freedom
## Multiple R-squared: 0.04568, Adjusted R-squared: -0.01129
## F-statistic: 0.8018 on 4 and 67 DF, p-value: 0.5283print("N.B. Lieber_104 has CONTROLS ONLY")## [1] "N.B. Lieber_104 has CONTROLS ONLY"summary(lm(OPC ~ age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_104")))##
## Call:
## lm(formula = OPC ~ age + age2 + sex, data = ctp.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(batch == "Lieber_104"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.63144 -0.14533 0.02343 0.12604 0.49079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.743e+00 9.298e-02 -18.747 <2e-16 ***
## age -1.046e-02 5.529e-03 -1.891 0.0652 .
## age2 1.634e-04 7.182e-05 2.275 0.0278 *
## sexM -7.200e-02 7.345e-02 -0.980 0.3323
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2529 on 44 degrees of freedom
## Multiple R-squared: 0.1538, Adjusted R-squared: 0.09607
## F-statistic: 2.665 on 3 and 44 DF, p-value: 0.05944print("N.B. Lieber_30 has SCHIZOPHRENIA PATIENTS ONLY")## [1] "N.B. Lieber_30 has SCHIZOPHRENIA PATIENTS ONLY"summary(lm(OPC ~ age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_30")))##
## Call:
## lm(formula = OPC ~ age + age2 + sex, data = ctp.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(batch == "Lieber_30"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33851 -0.14515 -0.01766 0.02969 0.65265
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.9325811 0.5828367 -3.316 0.00314 **
## age 0.0148115 0.0254726 0.581 0.56683
## age2 -0.0002005 0.0002714 -0.739 0.46796
## sexM -0.0562361 0.1057309 -0.532 0.60014
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2339 on 22 degrees of freedom
## Multiple R-squared: 0.06177, Adjusted R-squared: -0.06618
## F-statistic: 0.4828 on 3 and 22 DF, p-value: 0.6976summary(lm(OPC ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch == "Lieber_244")))##
## Call:
## lm(formula = OPC ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch == "Lieber_244"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8069 -0.1482 0.0037 0.1368 0.8977
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.929e+00 7.314e-02 -26.377 < 2e-16 ***
## dxSCZ 1.313e-01 4.319e-02 3.041 0.00266 **
## age -8.898e-04 4.155e-03 -0.214 0.83065
## age2 3.147e-05 5.738e-05 0.549 0.58392
## sexM 6.750e-02 4.242e-02 1.591 0.11303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2741 on 209 degrees of freedom
## Multiple R-squared: 0.0834, Adjusted R-squared: 0.06586
## F-statistic: 4.754 on 4 and 209 DF, p-value: 0.001085summary(lm(OPC ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289", "Lieber_244", "Lieber_315"))))##
## Call:
## lm(formula = OPC ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289",
## "Lieber_244", "Lieber_315")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.39371 -0.13318 0.00615 0.14620 0.89803
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.847e+00 5.143e-02 -35.919 < 2e-16 ***
## dxSCZ 6.797e-02 3.001e-02 2.265 0.02405 *
## age -3.144e-03 2.318e-03 -1.357 0.17568
## age2 6.883e-05 2.825e-05 2.437 0.01526 *
## sexM 1.540e-02 2.936e-02 0.525 0.60012
## batchLieber_289 -5.624e-02 3.260e-02 -1.725 0.08526 .
## batchLieber_315 1.025e-01 3.680e-02 2.785 0.00562 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2676 on 394 degrees of freedom
## Multiple R-squared: 0.09117, Adjusted R-squared: 0.07734
## F-statistic: 6.588 on 6 and 394 DF, p-value: 1.198e-06summary(lm(OPC ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289", "Lieber_244", "Lieber_315", "Lieber_30"))))##
## Call:
## lm(formula = OPC ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289",
## "Lieber_244", "Lieber_315", "Lieber_30")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.38816 -0.13186 0.00542 0.14419 0.89805
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.848e+00 5.069e-02 -36.449 < 2e-16 ***
## dxSCZ 7.057e-02 2.973e-02 2.373 0.01807 *
## age -2.872e-03 2.293e-03 -1.252 0.21120
## age2 6.278e-05 2.782e-05 2.257 0.02454 *
## sexM 1.447e-02 2.828e-02 0.512 0.60912
## batchLieber_289 -5.323e-02 3.232e-02 -1.647 0.10031
## batchLieber_30 1.045e-02 5.790e-02 0.180 0.85689
## batchLieber_315 1.039e-01 3.655e-02 2.843 0.00468 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2659 on 419 degrees of freedom
## Multiple R-squared: 0.08514, Adjusted R-squared: 0.06986
## F-statistic: 5.571 on 7 and 419 DF, p-value: 3.763e-06summary(lm(OPC ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289", "Lieber_244", "Lieber_315", "Lieber_30", "Lieber_104"))))##
## Call:
## lm(formula = OPC ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289",
## "Lieber_244", "Lieber_315", "Lieber_30", "Lieber_104")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.38908 -0.13300 0.00666 0.14377 0.90483
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.848e+00 5.330e-02 -34.678 < 2e-16 ***
## dxSCZ 6.994e-02 2.912e-02 2.402 0.01670 *
## age -3.851e-03 2.111e-03 -1.824 0.06885 .
## age2 7.521e-05 2.594e-05 2.899 0.00392 **
## sexM 3.734e-03 2.643e-02 0.141 0.88770
## batchLieber_244 2.396e-02 4.490e-02 0.534 0.59385
## batchLieber_289 -3.185e-02 4.763e-02 -0.669 0.50403
## batchLieber_30 3.609e-02 7.122e-02 0.507 0.61256
## batchLieber_315 1.264e-01 5.115e-02 2.471 0.01383 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2647 on 466 degrees of freedom
## Multiple R-squared: 0.08816, Adjusted R-squared: 0.0725
## F-statistic: 5.632 on 8 and 466 DF, p-value: 7.778e-07summary(lm(OPC ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>% filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289", "Lieber_244", "Lieber_315", "Lieber_30", "Lieber_104"))))##
## Call:
## lm(formula = OPC ~ dx + age + age2 + sex, data = ctp.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(batch %in% c("Lieber_289",
## "Lieber_244", "Lieber_315", "Lieber_30", "Lieber_104")))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.44013 -0.13243 0.00328 0.14222 0.90345
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.825e+00 4.434e-02 -41.159 < 2e-16 ***
## dxSCZ 7.634e-02 2.722e-02 2.805 0.00524 **
## age -3.647e-03 2.122e-03 -1.719 0.08629 .
## age2 6.935e-05 2.582e-05 2.686 0.00749 **
## sexM 7.149e-03 2.641e-02 0.271 0.78674
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2681 on 470 degrees of freedom
## Multiple R-squared: 0.05666, Adjusted R-squared: 0.04863
## F-statistic: 7.057 on 4 and 470 DF, p-value: 1.596e-057.1.1.3 ROSMAP
summary(lm(cbind(Exc, Inh, Astro, Endo, Micro, Oligo, OPC) ~ dx, data = ctp.clr.1e3 %>% filter(study == "ROSMAP")))## Response Exc :
##
## Call:
## lm(formula = Exc ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5375 -0.1346 -0.0053 0.1278 0.6560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.16747 0.01161 100.60 < 2e-16 ***
## dxCTL -0.04242 0.01520 -2.79 0.00541 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.201 on 717 degrees of freedom
## Multiple R-squared: 0.01074, Adjusted R-squared: 0.00936
## F-statistic: 7.784 on 1 and 717 DF, p-value: 0.00541
##
##
## Response Inh :
##
## Call:
## lm(formula = Inh ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.34983 -0.06825 -0.01172 0.06145 0.37696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.836294 0.005961 140.292 < 2e-16 ***
## dxCTL -0.025514 0.007809 -3.267 0.00114 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1032 on 717 degrees of freedom
## Multiple R-squared: 0.01467, Adjusted R-squared: 0.0133
## F-statistic: 10.68 on 1 and 717 DF, p-value: 0.001137
##
##
## Response Astro :
##
## Call:
## lm(formula = Astro ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.51884 -0.11213 -0.01432 0.10215 0.61453
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.76711 0.01001 76.663 <2e-16 ***
## dxCTL 0.01667 0.01311 1.272 0.204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1733 on 717 degrees of freedom
## Multiple R-squared: 0.002251, Adjusted R-squared: 0.0008596
## F-statistic: 1.618 on 1 and 717 DF, p-value: 0.2038
##
##
## Response Endo :
##
## Call:
## lm(formula = Endo ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.31397 -0.05869 0.00151 0.06707 0.42249
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.916359 0.005554 -165.00 < 2e-16 ***
## dxCTL 0.042850 0.007275 5.89 5.94e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09619 on 717 degrees of freedom
## Multiple R-squared: 0.04615, Adjusted R-squared: 0.04482
## F-statistic: 34.69 on 1 and 717 DF, p-value: 5.941e-09
##
##
## Response Micro :
##
## Call:
## lm(formula = Micro ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.65997 -0.09220 0.00041 0.09769 0.60584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.138531 0.009102 -125.088 <2e-16 ***
## dxCTL 0.022141 0.011923 1.857 0.0637 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1576 on 717 degrees of freedom
## Multiple R-squared: 0.004786, Adjusted R-squared: 0.003398
## F-statistic: 3.448 on 1 and 717 DF, p-value: 0.06372
##
##
## Response Oligo :
##
## Call:
## lm(formula = Oligo ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.81666 -0.14737 -0.01577 0.12565 0.86690
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.336109 0.012277 108.826 <2e-16 ***
## dxCTL 0.008825 0.016083 0.549 0.583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2127 on 717 degrees of freedom
## Multiple R-squared: 0.0004197, Adjusted R-squared: -0.0009744
## F-statistic: 0.3011 on 1 and 717 DF, p-value: 0.5834
##
##
## Response OPC :
##
## Call:
## lm(formula = OPC ~ dx, data = ctp.clr.1e3 %>% filter(study ==
## "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.85349 -0.15899 0.09949 0.26302 0.71132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.05209 0.02258 -90.885 <2e-16 ***
## dxCTL -0.02256 0.02958 -0.763 0.446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3911 on 717 degrees of freedom
## Multiple R-squared: 0.0008106, Adjusted R-squared: -0.000583
## F-statistic: 0.5817 on 1 and 717 DF, p-value: 0.4459summary(lm(cbind(Exc, Inh, Astro, Endo, Micro, Oligo, OPC) ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>% filter(study == "ROSMAP")))## Response Exc :
##
## Call:
## lm(formula = Exc ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.62110 -0.11409 -0.00245 0.11441 0.59751
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.8511518 1.7594377 -0.484 0.62870
## dxCTL -0.0444254 0.0149808 -2.965 0.00312 **
## age 0.0491061 0.0426217 1.152 0.24965
## age2 -0.0002837 0.0002571 -1.103 0.27022
## sexM -0.0441214 0.0150221 -2.937 0.00342 **
## batchROSMAP1 -0.1310765 0.0146939 -8.920 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1896 on 712 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1248, Adjusted R-squared: 0.1186
## F-statistic: 20.3 on 5 and 712 DF, p-value: < 2.2e-16
##
##
## Response Inh :
##
## Call:
## lm(formula = Inh ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.35631 -0.07035 -0.01002 0.05842 0.36748
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.050e+00 9.564e-01 1.098 0.272552
## dxCTL -2.834e-02 8.143e-03 -3.480 0.000533 ***
## age -4.418e-03 2.317e-02 -0.191 0.848817
## age2 2.384e-05 1.398e-04 0.171 0.864649
## sexM 8.513e-03 8.166e-03 1.043 0.297492
## batchROSMAP1 -1.922e-02 7.987e-03 -2.407 0.016343 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1031 on 712 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02497, Adjusted R-squared: 0.01812
## F-statistic: 3.647 on 5 and 712 DF, p-value: 0.002893
##
##
## Response Astro :
##
## Call:
## lm(formula = Astro ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.52432 -0.09742 0.00032 0.09707 0.50874
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.9243318 1.4388027 -1.337 0.1815
## dxCTL 0.0027631 0.0122508 0.226 0.8216
## age 0.0685744 0.0348545 1.967 0.0495 *
## age2 -0.0004177 0.0002103 -1.987 0.0474 *
## sexM 0.0092936 0.0122845 0.757 0.4496
## batchROSMAP1 -0.1603124 0.0120162 -13.341 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1551 on 712 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2069, Adjusted R-squared: 0.2013
## F-statistic: 37.15 on 5 and 712 DF, p-value: < 2.2e-16
##
##
## Response Endo :
##
## Call:
## lm(formula = Endo ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.32014 -0.05805 0.00400 0.06465 0.39716
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.125e-01 8.802e-01 -0.696 0.48669
## dxCTL 3.930e-02 7.494e-03 5.244 2.07e-07 ***
## age -5.096e-03 2.132e-02 -0.239 0.81118
## age2 2.165e-05 1.286e-04 0.168 0.86639
## sexM -3.065e-02 7.515e-03 -4.079 5.03e-05 ***
## batchROSMAP1 -1.995e-02 7.351e-03 -2.714 0.00681 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09485 on 712 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.07886, Adjusted R-squared: 0.07239
## F-statistic: 12.19 on 5 and 712 DF, p-value: 2.36e-11
##
##
## Response Micro :
##
## Call:
## lm(formula = Micro ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.68648 -0.09009 -0.00177 0.09708 0.62421
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7462917 1.4522618 1.202 0.22958
## dxCTL 0.0162101 0.0123654 1.311 0.19031
## age -0.0676964 0.0351805 -1.924 0.05472 .
## age2 0.0003945 0.0002123 1.859 0.06350 .
## sexM 0.0333366 0.0123995 2.689 0.00734 **
## batchROSMAP1 0.0027800 0.0121286 0.229 0.81877
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1565 on 712 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02601, Adjusted R-squared: 0.01917
## F-statistic: 3.802 on 5 and 712 DF, p-value: 0.002093
##
##
## Response Oligo :
##
## Call:
## lm(formula = Oligo ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.77402 -0.13535 -0.01136 0.12072 0.80663
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.262e+00 1.922e+00 0.657 0.512
## dxCTL 9.959e-04 1.637e-02 0.061 0.951
## age 4.090e-03 4.657e-02 0.088 0.930
## age2 -2.879e-05 2.810e-04 -0.102 0.918
## sexM 1.237e-02 1.641e-02 0.754 0.451
## batchROSMAP1 -1.006e-01 1.605e-02 -6.268 6.33e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2072 on 712 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.05397, Adjusted R-squared: 0.04733
## F-statistic: 8.124 on 5 and 712 DF, p-value: 1.808e-07
##
##
## Response OPC :
##
## Call:
## lm(formula = OPC ~ dx + age + age2 + sex + batch, data = ctp.clr.1e3 %>%
## filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.57100 -0.12318 0.05148 0.19277 0.83712
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.6706796 3.0883517 -0.217 0.828
## dxCTL 0.0134924 0.0262960 0.513 0.608
## age -0.0445606 0.0748142 -0.596 0.552
## age2 0.0002903 0.0004514 0.643 0.520
## sexM 0.0112604 0.0263684 0.427 0.669
## batchROSMAP1 0.4284120 0.0257924 16.610 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3328 on 712 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2813, Adjusted R-squared: 0.2763
## F-statistic: 55.74 on 5 and 712 DF, p-value: < 2.2e-167.1.2 clr, adjustment for oligodendrocytes
7.1.2.1 ASDbrain
summary(lm(cbind(Exc, Inh, Astro, Endo, Micro, OPC) ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID)))## Response Exc :
##
## Call:
## lm(formula = Exc ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.32825 -0.08405 0.02557 0.08385 0.23387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.91393 0.02486 36.770 < 2e-16 ***
## dxCTL 0.11309 0.03643 3.104 0.00299 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1384 on 56 degrees of freedom
## Multiple R-squared: 0.1468, Adjusted R-squared: 0.1316
## F-statistic: 9.637 on 1 and 56 DF, p-value: 0.002988
##
##
## Response Inh :
##
## Call:
## lm(formula = Inh ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23197 -0.09340 -0.02429 0.04625 0.45290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.92788 0.02606 35.611 <2e-16 ***
## dxCTL -0.01865 0.03819 -0.488 0.627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1451 on 56 degrees of freedom
## Multiple R-squared: 0.004241, Adjusted R-squared: -0.01354
## F-statistic: 0.2385 on 1 and 56 DF, p-value: 0.6272
##
##
## Response Astro :
##
## Call:
## lm(formula = Astro ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.57292 -0.08789 0.03716 0.14975 0.30203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.15600 0.03468 4.499 3.49e-05 ***
## dxCTL 0.01812 0.05082 0.357 0.723
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1931 on 56 degrees of freedom
## Multiple R-squared: 0.002265, Adjusted R-squared: -0.01555
## F-statistic: 0.1271 on 1 and 56 DF, p-value: 0.7228
##
##
## Response Endo :
##
## Call:
## lm(formula = Endo ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.121394 -0.057834 -0.001869 0.043912 0.177785
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.61465 0.01212 -50.702 <2e-16 ***
## dxCTL -0.01163 0.01777 -0.655 0.515
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0675 on 56 degrees of freedom
## Multiple R-squared: 0.007594, Adjusted R-squared: -0.01013
## F-statistic: 0.4285 on 1 and 56 DF, p-value: 0.5154
##
##
## Response Micro :
##
## Call:
## lm(formula = Micro ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23966 -0.06034 -0.01490 0.05979 0.26060
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.54527 0.01602 -34.03 < 2e-16 ***
## dxCTL -0.07422 0.02349 -3.16 0.00254 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08922 on 56 degrees of freedom
## Multiple R-squared: 0.1513, Adjusted R-squared: 0.1362
## F-statistic: 9.987 on 1 and 56 DF, p-value: 0.002543
##
##
## Response OPC :
##
## Call:
## lm(formula = OPC ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14102 -0.04735 -0.01266 0.03265 0.23185
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.83789 0.01265 -66.22 <2e-16 ***
## dxCTL -0.02671 0.01855 -1.44 0.155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07045 on 56 degrees of freedom
## Multiple R-squared: 0.03572, Adjusted R-squared: 0.0185
## F-statistic: 2.074 on 1 and 56 DF, p-value: 0.1554summary(lm(cbind(Exc, Inh, Astro, Endo, Micro, OPC) ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>% filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID)))## Response Exc :
##
## Call:
## lm(formula = Exc ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.35643 -0.07457 0.04598 0.09263 0.19777
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.975e-01 1.360e-01 5.127 4.41e-06 ***
## dxCTL 1.004e-01 3.875e-02 2.592 0.0124 *
## age 1.412e-02 6.872e-03 2.055 0.0449 *
## age2 -1.573e-04 8.429e-05 -1.867 0.0676 .
## sexM -5.897e-02 4.288e-02 -1.375 0.1749
## batchASDbrain2 -3.178e-02 5.787e-02 -0.549 0.5852
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.133 on 52 degrees of freedom
## Multiple R-squared: 0.2685, Adjusted R-squared: 0.1982
## F-statistic: 3.818 on 5 and 52 DF, p-value: 0.005083
##
##
## Response Inh :
##
## Call:
## lm(formula = Inh ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.16288 -0.08607 -0.04305 0.05087 0.43783
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.320e-01 1.459e-01 6.387 4.69e-08 ***
## dxCTL -1.953e-02 4.156e-02 -0.470 0.6404
## age -5.631e-03 7.371e-03 -0.764 0.4484
## age2 8.096e-05 9.041e-05 0.895 0.3747
## sexM 9.120e-02 4.599e-02 1.983 0.0527 .
## batchASDbrain2 1.782e-02 6.207e-02 0.287 0.7752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1426 on 52 degrees of freedom
## Multiple R-squared: 0.1062, Adjusted R-squared: 0.02022
## F-statistic: 1.235 on 5 and 52 DF, p-value: 0.3061
##
##
## Response Astro :
##
## Call:
## lm(formula = Astro ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.56849 -0.11162 0.04274 0.14202 0.27512
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.013e-01 2.026e-01 0.500 0.619
## dxCTL 1.026e-02 5.770e-02 0.178 0.860
## age 4.758e-03 1.023e-02 0.465 0.644
## age2 -4.911e-05 1.255e-04 -0.391 0.697
## sexM -5.058e-02 6.385e-02 -0.792 0.432
## batchASDbrain2 -6.758e-03 8.618e-02 -0.078 0.938
##
## Residual standard error: 0.198 on 52 degrees of freedom
## Multiple R-squared: 0.02522, Adjusted R-squared: -0.06851
## F-statistic: 0.2691 on 5 and 52 DF, p-value: 0.928
##
##
## Response Endo :
##
## Call:
## lm(formula = Endo ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12373 -0.04755 -0.00116 0.04032 0.17173
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.417e-01 6.950e-02 -7.794 2.7e-10 ***
## dxCTL -7.020e-03 1.980e-02 -0.355 0.724
## age -2.943e-03 3.511e-03 -0.838 0.406
## age2 2.434e-05 4.306e-05 0.565 0.574
## sexM -4.252e-03 2.191e-02 -0.194 0.847
## batchASDbrain2 -3.498e-03 2.957e-02 -0.118 0.906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06794 on 52 degrees of freedom
## Multiple R-squared: 0.06643, Adjusted R-squared: -0.02334
## F-statistic: 0.74 on 5 and 52 DF, p-value: 0.597
##
##
## Response Micro :
##
## Call:
## lm(formula = Micro ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.247334 -0.040037 -0.002593 0.037348 0.182667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.499e-01 7.965e-02 -4.392 5.53e-05 ***
## dxCTL -5.545e-02 2.269e-02 -2.444 0.0179 *
## age -9.351e-03 4.024e-03 -2.324 0.0241 *
## age2 8.194e-05 4.935e-05 1.660 0.1029
## sexM 1.720e-02 2.510e-02 0.685 0.4963
## batchASDbrain2 1.109e-02 3.388e-02 0.327 0.7447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07786 on 52 degrees of freedom
## Multiple R-squared: 0.3999, Adjusted R-squared: 0.3422
## F-statistic: 6.929 on 5 and 52 DF, p-value: 5.039e-05
##
##
## Response OPC :
##
## Call:
## lm(formula = OPC ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "ASDbrain") %>% filter(!IID %in% asd_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.16714 -0.04313 -0.01055 0.03759 0.22117
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.392e-01 7.384e-02 -11.365 1.04e-15 ***
## dxCTL -2.868e-02 2.103e-02 -1.364 0.179
## age -9.570e-04 3.730e-03 -0.257 0.799
## age2 1.919e-05 4.575e-05 0.419 0.677
## sexM 5.408e-03 2.327e-02 0.232 0.817
## batchASDbrain2 1.313e-02 3.141e-02 0.418 0.678
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07218 on 52 degrees of freedom
## Multiple R-squared: 0.06018, Adjusted R-squared: -0.03018
## F-statistic: 0.666 on 5 and 52 DF, p-value: 0.65097.1.2.2 LIBD
summary(lm(cbind(Exc, Inh, Astro, Endo, Micro, OPC) ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID)))## Response Exc :
##
## Call:
## lm(formula = Exc ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.30930 -0.06894 0.02787 0.11734 0.69250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.47625 0.01475 100.059 <2e-16 ***
## dxSCZ -0.02374 0.02175 -1.091 0.276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2173 on 400 degrees of freedom
## Multiple R-squared: 0.002969, Adjusted R-squared: 0.0004763
## F-statistic: 1.191 on 1 and 400 DF, p-value: 0.2758
##
##
## Response Inh :
##
## Call:
## lm(formula = Inh ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.40167 -0.08593 -0.01063 0.06652 0.76092
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.43113 0.01066 134.285 < 2e-16 ***
## dxSCZ -0.04703 0.01571 -2.993 0.00293 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.157 on 400 degrees of freedom
## Multiple R-squared: 0.02191, Adjusted R-squared: 0.01947
## F-statistic: 8.961 on 1 and 400 DF, p-value: 0.00293
##
##
## Response Astro :
##
## Call:
## lm(formula = Astro ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.67228 -0.10054 0.01815 0.11845 0.56670
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.51435 0.01285 40.024 <2e-16 ***
## dxSCZ 0.02237 0.01894 1.181 0.238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1893 on 400 degrees of freedom
## Multiple R-squared: 0.003473, Adjusted R-squared: 0.0009813
## F-statistic: 1.394 on 1 and 400 DF, p-value: 0.2385
##
##
## Response Endo :
##
## Call:
## lm(formula = Endo ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23296 -0.06172 -0.01918 0.05239 0.37768
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.736283 0.006545 -112.490 <2e-16 ***
## dxSCZ 0.008716 0.009648 0.903 0.367
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09642 on 400 degrees of freedom
## Multiple R-squared: 0.002036, Adjusted R-squared: -0.0004589
## F-statistic: 0.816 on 1 and 400 DF, p-value: 0.3669
##
##
## Response Micro :
##
## Call:
## lm(formula = Micro ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.39508 -0.08838 -0.00046 0.07998 0.45376
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.847647 0.008924 -94.989 < 2e-16 ***
## dxSCZ -0.044959 0.013154 -3.418 0.000696 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1315 on 400 degrees of freedom
## Multiple R-squared: 0.02837, Adjusted R-squared: 0.02595
## F-statistic: 11.68 on 1 and 400 DF, p-value: 0.0006962
##
##
## Response OPC :
##
## Call:
## lm(formula = OPC ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2837 -0.1360 0.0154 0.1333 0.7990
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.83781 0.01659 -110.809 < 2e-16 ***
## dxSCZ 0.08464 0.02445 3.462 0.000594 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2443 on 400 degrees of freedom
## Multiple R-squared: 0.02909, Adjusted R-squared: 0.02666
## F-statistic: 11.99 on 1 and 400 DF, p-value: 0.0005941summary(lm(cbind(Exc, Inh, Astro, Endo, Micro, OPC) ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>% filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID)))## Response Exc :
##
## Call:
## lm(formula = Exc ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.18825 -0.06449 0.03014 0.10607 0.63626
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.280e+00 8.807e-02 14.534 < 2e-16 ***
## dxSCZ -3.594e-02 2.377e-02 -1.512 0.13128
## age 9.243e-03 3.324e-03 2.781 0.00568 **
## age2 -6.514e-05 3.386e-05 -1.924 0.05510 .
## sexM -1.136e-03 2.294e-02 -0.050 0.96052
## batchLieber_244 -8.171e-02 4.304e-02 -1.898 0.05838 .
## batchLieber_289 -6.174e-02 4.364e-02 -1.415 0.15795
## batchLieber_30 -5.257e-02 6.127e-02 -0.858 0.39145
## batchLieber_315 -1.175e-01 4.754e-02 -2.472 0.01387 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2111 on 393 degrees of freedom
## Multiple R-squared: 0.07575, Adjusted R-squared: 0.05694
## F-statistic: 4.026 on 8 and 393 DF, p-value: 0.0001299
##
##
## Response Inh :
##
## Call:
## lm(formula = Inh ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33226 -0.08571 -0.02126 0.05564 0.76881
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.427e+00 6.066e-02 23.528 < 2e-16 ***
## dxSCZ -5.332e-02 1.637e-02 -3.257 0.00122 **
## age 1.005e-04 2.290e-03 0.044 0.96502
## age2 7.817e-06 2.332e-05 0.335 0.73770
## sexM 1.630e-02 1.580e-02 1.032 0.30285
## batchLieber_244 9.977e-03 2.965e-02 0.337 0.73665
## batchLieber_289 -3.446e-02 3.006e-02 -1.146 0.25238
## batchLieber_30 3.523e-02 4.221e-02 0.835 0.40444
## batchLieber_315 -1.645e-01 3.274e-02 -5.025 7.65e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1454 on 393 degrees of freedom
## Multiple R-squared: 0.1755, Adjusted R-squared: 0.1587
## F-statistic: 10.46 on 8 and 393 DF, p-value: 2.57e-13
##
##
## Response Astro :
##
## Call:
## lm(formula = Astro ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.64753 -0.09570 0.01925 0.11307 0.57792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.553e-01 7.797e-02 5.840 1.1e-08 ***
## dxSCZ 2.692e-02 2.104e-02 1.279 0.202
## age 3.906e-03 2.943e-03 1.327 0.185
## age2 -4.607e-05 2.998e-05 -1.537 0.125
## sexM -2.093e-02 2.031e-02 -1.031 0.303
## batchLieber_244 -2.738e-02 3.811e-02 -0.719 0.473
## batchLieber_289 1.406e-02 3.864e-02 0.364 0.716
## batchLieber_30 -4.280e-02 5.425e-02 -0.789 0.431
## batchLieber_315 6.712e-02 4.209e-02 1.595 0.112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1869 on 393 degrees of freedom
## Multiple R-squared: 0.04565, Adjusted R-squared: 0.02622
## F-statistic: 2.35 on 8 and 393 DF, p-value: 0.01777
##
##
## Response Endo :
##
## Call:
## lm(formula = Endo ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.22176 -0.06171 -0.00726 0.04588 0.34506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.661e-01 3.748e-02 -17.773 < 2e-16 ***
## dxSCZ 2.095e-02 1.011e-02 2.072 0.038947 *
## age -1.824e-03 1.415e-03 -1.290 0.197878
## age2 7.491e-08 1.441e-05 0.005 0.995855
## sexM -2.522e-02 9.762e-03 -2.583 0.010151 *
## batchLieber_244 9.900e-03 1.832e-02 0.540 0.589163
## batchLieber_289 6.355e-02 1.857e-02 3.422 0.000687 ***
## batchLieber_30 -3.273e-02 2.608e-02 -1.255 0.210191
## batchLieber_315 4.631e-02 2.023e-02 2.289 0.022603 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08984 on 393 degrees of freedom
## Multiple R-squared: 0.1488, Adjusted R-squared: 0.1314
## F-statistic: 8.584 on 8 and 393 DF, p-value: 8.451e-11
##
##
## Response Micro :
##
## Call:
## lm(formula = Micro ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.36459 -0.05973 -0.01065 0.06109 0.36642
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.132e-01 4.529e-02 -13.541 < 2e-16 ***
## dxSCZ -2.086e-02 1.222e-02 -1.707 0.0887 .
## age -8.573e-03 1.709e-03 -5.016 8.01e-07 ***
## age2 4.164e-05 1.741e-05 2.392 0.0172 *
## sexM 2.689e-02 1.180e-02 2.280 0.0232 *
## batchLieber_244 4.293e-02 2.213e-02 1.940 0.0531 .
## batchLieber_289 4.059e-02 2.244e-02 1.809 0.0713 .
## batchLieber_30 5.134e-02 3.151e-02 1.629 0.1040
## batchLieber_315 3.597e-02 2.445e-02 1.472 0.1419
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1086 on 393 degrees of freedom
## Multiple R-squared: 0.3489, Adjusted R-squared: 0.3357
## F-statistic: 26.33 on 8 and 393 DF, p-value: < 2.2e-16
##
##
## Response OPC :
##
## Call:
## lm(formula = OPC ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "LIBD") %>% filter(!IID %in% scz_exclude$IID))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.29501 -0.12588 0.01039 0.13164 0.70331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.883e+00 9.917e-02 -18.990 <2e-16 ***
## dxSCZ 6.224e-02 2.676e-02 2.326 0.0205 *
## age -2.852e-03 3.743e-03 -0.762 0.4465
## age2 6.167e-05 3.813e-05 1.618 0.1066
## sexM 4.090e-03 2.583e-02 0.158 0.8743
## batchLieber_244 4.628e-02 4.847e-02 0.955 0.3402
## batchLieber_289 -2.201e-02 4.914e-02 -0.448 0.6545
## batchLieber_30 4.152e-02 6.900e-02 0.602 0.5476
## batchLieber_315 1.327e-01 5.353e-02 2.478 0.0136 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2377 on 393 degrees of freedom
## Multiple R-squared: 0.09689, Adjusted R-squared: 0.07851
## F-statistic: 5.27 on 8 and 393 DF, p-value: 2.752e-067.1.2.3 ROSMAP
summary(lm(cbind(Exc, Inh, Astro, Endo, Micro, OPC) ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study == "ROSMAP")))## Response Exc :
##
## Call:
## lm(formula = Exc ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.48817 -0.12401 -0.01904 0.11926 0.79840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.74318 0.01137 153.269 <2e-16 ***
## dxCTL -0.02783 0.01490 -1.868 0.0622 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.197 on 717 degrees of freedom
## Multiple R-squared: 0.004843, Adjusted R-squared: 0.003455
## F-statistic: 3.489 on 1 and 717 DF, p-value: 0.06217
##
##
## Response Inh :
##
## Call:
## lm(formula = Inh ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.51343 -0.07707 -0.01463 0.06899 0.42160
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.412007 0.006460 218.587 <2e-16 ***
## dxCTL -0.010929 0.008462 -1.292 0.197
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1119 on 717 degrees of freedom
## Multiple R-squared: 0.002321, Adjusted R-squared: 0.0009296
## F-statistic: 1.668 on 1 and 717 DF, p-value: 0.1969
##
##
## Response Astro :
##
## Call:
## lm(formula = Astro ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.50563 -0.12266 -0.01961 0.10709 0.68974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.81328 0.01073 75.800 <2e-16 ***
## dxCTL 0.01159 0.01405 0.824 0.41
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1858 on 717 degrees of freedom
## Multiple R-squared: 0.0009467, Adjusted R-squared: -0.0004466
## F-statistic: 0.6795 on 1 and 717 DF, p-value: 0.41
##
##
## Response Endo :
##
## Call:
## lm(formula = Endo ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33562 -0.05981 0.00009 0.06321 0.49624
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.870188 0.005565 -156.37 < 2e-16 ***
## dxCTL 0.037764 0.007290 5.18 2.88e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09639 on 717 degrees of freedom
## Multiple R-squared: 0.03608, Adjusted R-squared: 0.03473
## F-statistic: 26.84 on 1 and 717 DF, p-value: 2.881e-07
##
##
## Response Micro :
##
## Call:
## lm(formula = Micro ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.60978 -0.08418 -0.00458 0.09132 0.57273
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.092360 0.008384 -130.285 <2e-16 ***
## dxCTL 0.017055 0.010983 1.553 0.121
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1452 on 717 degrees of freedom
## Multiple R-squared: 0.003352, Adjusted R-squared: 0.001962
## F-statistic: 2.411 on 1 and 717 DF, p-value: 0.1209
##
##
## Response OPC :
##
## Call:
## lm(formula = OPC ~ dx, data = ctp_adjoligo.clr.1e3 %>% filter(study ==
## "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.79277 -0.15594 0.09778 0.24795 0.67619
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.00592 0.02162 -92.768 <2e-16 ***
## dxCTL -0.02764 0.02833 -0.976 0.329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3745 on 717 degrees of freedom
## Multiple R-squared: 0.001327, Adjusted R-squared: -6.62e-05
## F-statistic: 0.9525 on 1 and 717 DF, p-value: 0.3294summary(lm(cbind(Exc, Inh, Astro, Endo, Micro, OPC) ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>% filter(study == "ROSMAP")))## Response Exc :
##
## Call:
## lm(formula = Exc ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.55841 -0.11794 -0.00286 0.11007 0.69040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0777728 1.6910236 -0.046 0.9633
## dxCTL -0.0326284 0.0143983 -2.266 0.0237 *
## age 0.0451458 0.0409644 1.102 0.2708
## age2 -0.0002629 0.0002471 -1.064 0.2878
## sexM -0.0319264 0.0144380 -2.211 0.0273 *
## batchROSMAP1 -0.1533422 0.0141226 -10.858 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1822 on 712 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1543, Adjusted R-squared: 0.1483
## F-statistic: 25.98 on 5 and 712 DF, p-value: < 2.2e-16
##
##
## Response Inh :
##
## Call:
## lm(formula = Inh ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.48842 -0.07664 -0.01010 0.06065 0.40217
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.824e+00 1.018e+00 1.792 0.0736 .
## dxCTL -1.654e-02 8.666e-03 -1.908 0.0567 .
## age -8.378e-03 2.465e-02 -0.340 0.7341
## age2 4.467e-05 1.488e-04 0.300 0.7640
## sexM 2.071e-02 8.690e-03 2.383 0.0174 *
## batchROSMAP1 -4.149e-02 8.500e-03 -4.881 1.3e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1097 on 712 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.04505, Adjusted R-squared: 0.03835
## F-statistic: 6.718 on 5 and 712 DF, p-value: 3.954e-06
##
##
## Response Astro :
##
## Call:
## lm(formula = Astro ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.57579 -0.10379 0.00002 0.10353 0.57667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.9954570 1.5381785 -1.297 0.1950
## dxCTL -0.0028865 0.0130969 -0.220 0.8256
## age 0.0715771 0.0372618 1.921 0.0551 .
## age2 -0.0004354 0.0002248 -1.937 0.0532 .
## sexM 0.0062888 0.0131330 0.479 0.6322
## batchROSMAP1 -0.1743368 0.0128461 -13.571 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1658 on 712 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2106, Adjusted R-squared: 0.2051
## F-statistic: 37.99 on 5 and 712 DF, p-value: < 2.2e-16
##
##
## Response Endo :
##
## Call:
## lm(formula = Endo ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33845 -0.05798 0.00350 0.06103 0.46024
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.837e-01 8.716e-01 -0.784 0.433
## dxCTL 3.365e-02 7.421e-03 4.534 6.78e-06 ***
## age -2.093e-03 2.111e-02 -0.099 0.921
## age2 4.038e-06 1.274e-04 0.032 0.975
## sexM -3.366e-02 7.442e-03 -4.523 7.14e-06 ***
## batchROSMAP1 -3.397e-02 7.279e-03 -4.667 3.64e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09393 on 712 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.09086, Adjusted R-squared: 0.08448
## F-statistic: 14.23 on 5 and 712 DF, p-value: 2.714e-13
##
##
## Response Micro :
##
## Call:
## lm(formula = Micro ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.62960 -0.08482 -0.00259 0.09109 0.58138
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.6751665 1.3364847 1.253 0.21047
## dxCTL 0.0105606 0.0113796 0.928 0.35371
## age -0.0646936 0.0323758 -1.998 0.04607 *
## age2 0.0003769 0.0001953 1.929 0.05407 .
## sexM 0.0303318 0.0114109 2.658 0.00803 **
## batchROSMAP1 -0.0112445 0.0111616 -1.007 0.31408
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.144 on 712 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02649, Adjusted R-squared: 0.01965
## F-statistic: 3.875 on 5 and 712 DF, p-value: 0.001798
##
##
## Response OPC :
##
## Call:
## lm(formula = OPC ~ dx + age + age2 + sex + batch, data = ctp_adjoligo.clr.1e3 %>%
## filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.51132 -0.11639 0.04858 0.18850 0.77992
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.7418047 2.9456009 -0.252 0.801
## dxCTL 0.0078429 0.0250805 0.313 0.755
## age -0.0415578 0.0713561 -0.582 0.560
## age2 0.0002727 0.0004305 0.633 0.527
## sexM 0.0082556 0.0251496 0.328 0.743
## batchROSMAP1 0.4143876 0.0246002 16.845 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3174 on 712 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2875, Adjusted R-squared: 0.2825
## F-statistic: 57.46 on 5 and 712 DF, p-value: < 2.2e-167.2 Neurons vs glia (no clr)
summary(lm(Glial ~ dx, data = ctp %>% filter(study == "ASDbrain")))##
## Call:
## lm(formula = Glial ~ dx, data = ctp %>% filter(study == "ASDbrain"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08559 -0.03815 -0.01436 0.02309 0.16634
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.588293 0.008799 66.856 <2e-16 ***
## dxCTL -0.003401 0.013354 -0.255 0.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0577 on 74 degrees of freedom
## Multiple R-squared: 0.0008757, Adjusted R-squared: -0.01263
## F-statistic: 0.06486 on 1 and 74 DF, p-value: 0.7997summary(lm(Glial ~ dx, data = ctp %>% filter(study == "LIBD")))##
## Call:
## lm(formula = Glial ~ dx, data = ctp %>% filter(study == "LIBD"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.132700 -0.043370 -0.007019 0.033785 0.240464
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.625528 0.003815 163.962 <2e-16 ***
## dxSCZ -0.003258 0.006097 -0.534 0.593
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06486 on 473 degrees of freedom
## Multiple R-squared: 0.0006033, Adjusted R-squared: -0.00151
## F-statistic: 0.2855 on 1 and 473 DF, p-value: 0.5934summary(lm(Glial ~ dx, data = ctp %>% filter(study == "ROSMAP")))##
## Call:
## lm(formula = Glial ~ dx, data = ctp %>% filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.109166 -0.026815 -0.002024 0.023203 0.179770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.516193 0.002345 220.162 < 2e-16 ***
## dxCTL 0.011713 0.003071 3.814 0.000149 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04061 on 717 degrees of freedom
## Multiple R-squared: 0.01988, Adjusted R-squared: 0.01851
## F-statistic: 14.54 on 1 and 717 DF, p-value: 0.0001487#summary(lm(mcc_NeuN_neg ~ dx, data = ctp))
#summary(lm(h_NeuN_neg ~ dx, data = ctp))
summary(lm(Glial ~ dx + age + age2 + sex + batch, data = ctp %>% filter(study =="ASDbrain")))##
## Call:
## lm(formula = Glial ~ dx + age + age2 + sex + batch, data = ctp %>%
## filter(study == "ASDbrain"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08206 -0.03987 -0.01735 0.02535 0.16575
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.290e-01 2.805e-02 18.861 <2e-16 ***
## dxCTL -6.311e-03 1.604e-02 -0.394 0.6951
## age 2.568e-03 1.432e-03 1.793 0.0772 .
## age2 -2.918e-05 1.828e-05 -1.596 0.1150
## sexM 2.021e-02 1.674e-02 1.207 0.2315
## batchASDbrain2 1.501e-02 2.296e-02 0.654 0.5154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05708 on 70 degrees of freedom
## Multiple R-squared: 0.07513, Adjusted R-squared: 0.009064
## F-statistic: 1.137 on 5 and 70 DF, p-value: 0.349summary(lm(Glial ~ dx + age + age2 + sex + batch, data = ctp %>% filter(study =="LIBD")))##
## Call:
## lm(formula = Glial ~ dx + age + age2 + sex + batch, data = ctp %>%
## filter(study == "LIBD"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.126317 -0.042633 -0.007354 0.032134 0.244239
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.898e-01 1.258e-02 46.886 < 2e-16 ***
## dxSCZ -1.788e-02 6.872e-03 -2.601 0.00958 **
## age 1.764e-03 4.983e-04 3.540 0.00044 ***
## age2 -1.222e-05 6.123e-06 -1.996 0.04655 *
## sexM -1.711e-03 6.237e-03 -0.274 0.78394
## batchLieber_244 4.510e-03 1.060e-02 0.426 0.67055
## batchLieber_289 -1.589e-02 1.124e-02 -1.414 0.15797
## batchLieber_30 1.887e-02 1.681e-02 1.123 0.26209
## batchLieber_315 -2.463e-02 1.207e-02 -2.041 0.04184 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06246 on 466 degrees of freedom
## Multiple R-squared: 0.08682, Adjusted R-squared: 0.07114
## F-statistic: 5.538 on 8 and 466 DF, p-value: 1.049e-06summary(lm(Glial ~ dx + age + age2 + sex + batch, data = ctp %>% filter(study =="ROSMAP")))##
## Call:
## lm(formula = Glial ~ dx + age + age2 + sex + batch, data = ctp %>%
## filter(study == "ROSMAP"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.110572 -0.025777 -0.002267 0.024340 0.186223
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.176e-01 3.742e-01 1.651 0.09927 .
## dxCTL 1.034e-02 3.186e-03 3.245 0.00123 **
## age -2.060e-03 9.064e-03 -0.227 0.82029
## age2 1.048e-05 5.468e-05 0.192 0.84808
## sexM 7.630e-03 3.195e-03 2.389 0.01717 *
## batchROSMAP1 -5.950e-03 3.125e-03 -1.904 0.05730 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04032 on 712 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.03606, Adjusted R-squared: 0.02929
## F-statistic: 5.326 on 5 and 712 DF, p-value: 8.156e-05#summary(lm(mcc_NeuN_neg ~ dx + age + sex + batch, data = ctp))
#summary(lm(h_NeuN_neg ~ dx + age + sex + batch, data = ctp))